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南京信息工程大学自动化学院导师教师师资介绍简介-刘光灿
本站小编 Free考研考试/2021-03-28
其他栏目
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
手机版
欢迎您访问我们的网站，您是第 位访客
其他栏目
基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
科学研究
当前位置 : 中文主页 >> 科学研究
手机版
欢迎您访问我们的网站，您是第 位访客
其他栏目
基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
研究领域
当前位置 : 中文主页 >> 科学研究 >> 研究领域
手机版
欢迎您访问我们的网站，您是第 位访客
其他栏目
基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
论文成果
当前位置 : 中文主页 >> 科学研究 >> 论文成果
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手机版
欢迎您访问我们的网站，您是第 位访客
其他栏目
基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
专利
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手机版
欢迎您访问我们的网站，您是第 位访客
其他栏目
基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
著作成果
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其他栏目
基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
科研项目
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手机版
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其他栏目
基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
教学研究
当前位置 : 中文主页 >> 教学研究
手机版
欢迎您访问我们的网站，您是第 位访客
其他栏目
基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
教学资源
当前位置 : 中文主页 >> 教学研究 >> 教学资源
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手机版
欢迎您访问我们的网站，您是第 位访客
其他栏目
基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
授课信息
当前位置 : 中文主页 >> 教学研究 >> 授课信息
手机版
欢迎您访问我们的网站，您是第 位访客
其他栏目
基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
教学成果
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基本信息
教师姓名：刘光灿
职称：教授
所在单位：自动化学院
学术荣誉：
国家杰出/优秀青年基金获得者
个人简介
学习与工作经历：教育经历：
2000.9 至 2004.7上海交通大学数学与应用数学本科
2004.9 至2010.7上海交通大学计算机科学与技术博士
工作经历：
2010.9至 2011.12National University of SingaporeResearch Fellow
2012.2 至 2013.2 University of Illinois at Urbana-Champaign Research Associate
2013.5 至 2014.7 Cornell University Research Associate
2014.8 至 今南京信息工程大学 教授
社会兼职：担任国际SCI期刊Neurocomputing的编委、10多个国际主流会议的程序委员会委员、及20余种国际主流期刊的论文评审专家。研究领域：模式识别、计算机视觉、图像处理、数据挖掘、多媒体
科研成果：主要项目：
[1] NSFC **, 国家自然科学基金青年项目, 2016 - 2018, 24.4万
[2] NSFC **, 国家自然科学基金优秀青年基金项目, 2017 - 2019, 150万
[3] BK**, 江苏省自然科学基金****基金项目, 2017 - 2019, 100万
主要论文：
[1] Guangcan Liu, Qingshan Liu, and Ping Li. Blessing of Dimensionality: Recovering Mixture Data via Dictionary Pursuit. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI),2016.
[2] Guangcan Liu and Ping Li. Low-Rank Matrix Completion in the Presence of High Coherence. IEEE Transactions on Signal Processing (T-SP), 2016.
[3] Guangcan Liu, Xuan Xu, Jinhui Tang, Qingshan Liu, Shuicheng Yan. A Deterministic Analysis for LRR. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 38, no.3, pp. 417-430, 2016.
[4] Guangcan Liu, Shiyu Chang, and Yi Ma. Blind Image Deblurring Using Spectral Properties of Convolution Operators. IEEE Transactions on Image Processing (T-IP), vol. 23, no.12, pp. 5047-5056, 2014.
[5] Guangcan Liu and Ping Li, Recovery of Coherent Data via Low-Rank Dictionary Pursuit, Advances in Neural Information Processing Systems (NIPS), pp. 1206-1214, Montreal, Canada,2014.
[6] Guangcan Liu, Zhouchen Lin, Shuicheng Yan,Ju Sun, Yong Yu, and Yi Ma. Robust Recovery of Subspace Structures by Low-Rank Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol. 35, no. 1, pp.171-184, 2013.
[7] Guangcan Liu and Shuicheng Yan. Active Subspace: Towards Scalable Low-Rank Learning. Neural Computation, vol. 24, no. 12, pp. 3371-3394, 2012.
[8] Guangcan Liu, Huan Xu, and Shuicheng Yan. Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation. International conference on Artificial Intelligence and Statistics (AISTATS), vol. 22, pp. 703-711, Canary Islands, Spain, 2012.
[9] Guangcan Liu and Shuicheng Yan. Latent Low-Rank Representation for Subspace Segmentation and Feature Extraction. International Conference on Computer Vision (ICCV), pp. 1615-1622, Barcelona, Spain, 2011.
[10] Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang , Unsupervised Object Segmentation with a Hybrid Graph Model (HGM), IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), vol.32, no.5, pp.910-924, 2010.
[11] Guangcan Liu, Zhouchen Lin, and Yong Yu, Robust Subspace Segmentation by Low-Rank Representation. International Conference on Machine Learning (ICML), pp. 663-670, Haifa, Isreal, June 2010.
[12] Guangcan Liu, Zhouchen Lin, Yong Yu, Xiaoou Tang. Radon Representation Based Feature Descriptor for Texture Classification; IEEE Transactions on Image Processing (T-IP), vol. 18, no. 5, pp. 921-928, 2009.
[13] Guangcan Liu, Zhouchen Lin, Yong Yu. Multi-Output Regression on the Output Manifold. Pattern Recognition (PR), vol.42, no.11, pp. 2737-2743, 2009.
[14] Guangcan Liu, Zhouchen Lin, Xiaoou Tang, Yong Yu. A Hybrid Graph Model for Unsupervised Object Segmentation, International Conference on Computer Vision (ICCV), pp. 1-8, Rio de Janeiro, Brazil, 2007.
[15] Guangcan Liu, Yong Yu, Xing Zhu. A Learning-Based Term-weighting Approach for Information Retrieval; American Association for Artificial Intelligence (AAAI), vol. 20, no. 3, pp. 1418-1423, Pittsburgh, USA, 2005.
荣誉：1、中华人民共和国教育部自然科学奖、二等奖 (排序2)，2016
2、上海市研究生优秀论文成果奖(排序1)， 20153、微软****，2006
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