袁浩亮 YUANHAOLIANG 副教授

所属学院：
自动化学院

导师类别：
硕士生导师

科研方向：
机器学习、模式识别、计算机视觉

联系方式：
haoliangyuan@gdut.edu.cn

硕士招生学院：
自动化学院




个人简述 (限300字)
袁浩亮，男，1986年生，澳门大学软件工程博士学位，青年****特聘副教授，硕士生导师。主要从事机器学习、模式识别、计算机视觉等研究。现加入赖来利教授团队，主要从事人工智能在智能电网中的研究工作。




学科领域
l科学学位：电气工程
l专业学位：电气工程




教育背景
2005.09-2009.06 湖北大学 数学与应用数学 学士学位
2009.09-2012.06 湖北大学 应用数学 硕士学位
2012.09-2016.04 澳门大学 软件工程 博士学位

工作经历


主持项目





主要著作
无




主要论文
期刊论文
1.H. Yuan, J. Li, L. L. Lai, and Y. Y. Tang, Joint sparse matrix regression and nonnegative spectral analysis for two-dimensional unsupervised feature selection, Pattern Recognition, DOI: 10.1016/j.patcog.2019.01.014.
2.H. Yuan, J. Zheng, L. L. Lai, and Y. Y. Tang, A constrained least squares regression model, Information Sciences, vol. 429, pp. 247-259, 2018.
3.H. Yuan, J. Zheng, L. L. Lai, and Y. Y. Tang, Sparse structural feature selection for multitarget regression, Knowledge-Based Systems, vol. 160, no. 15, pp. 200-209, 2018.
4. F. Y. Xu, B. Huang, X. Cun, F. Wang,H. Yuan, L. L. Lai and A. Vaccaro, Classifier economics of Semi-Intrusive Load Monitoring, International Journal of Electrical Power & Energy Systems, vol. 103, pp. 224-232, 2018. (Corresponding authors)
5.H. Yuan, X. Li, F. Xu, Y. Wang, L. L. Lai, and Y. Y. Tang, A collaborative-competitive representation based classifier model, Neurocomputing, vol. 275, pp. 627-635, 2018.
6.H. Yuan, J. Li, L. L. Lai, and Y. Y. Tang, Graph-based multiple rank regression for image classification, Neurocomputing, vol. 315, no. 13, pp. 394-404, 2018.
7.H. Yuan, J. Zheng, L. L. Lai, and Y. Y. Tang, Semi-supervised graph-based retargeted least squares regression. Signal Processing, vol. 142, pp. 188-193, 2018.
8.H. Yuan, Robust patch-based sparse representation for hyperspectral image classification, International Journal of Wavelets, Multiresolution and Information Processing, vol. 15, no. 3, pp. 1-15, 2017.
9.H. Yuanand L. L. Lai, Robust subspace learning method for hyperspectral image classification, International Journal of Wavelets, Multiresolution and Information Processing, vol. 15, no. 6, pp. 1-21, 2017.
10.H. Yuanand Y. Y. Tang, Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification, IEEE Transactions on Cybernetics, vol. 47, no. 5, pp. 934-945, 2017.
11. Y. Y. Tang, Y. Lu, andH. Yuan, Hyperspectral Image Classification Based on Three-Dimensional Scattering Wavelet Transform, IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 5, pp. 2467-2480, 2015. (Corresponding author)
12.H. Yuanand Y. Y. Tang, Learning with Hypergraph for Hyperspectral Image Feature Extraction, IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 8, pp. 1695-1699, 2015.
13.H. Yuanand Y. Y. Tang, Sparse Representation Based on Set-to-set Distance for Hyperspectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 6, pp. 2464-2472, 2015.
14. Y. Y. Tang,H. Yuan, and L. Li, Manifold-Based Sparse Representation for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 12, pp. 7606-7618, 2014.
15.H. Yuan, Y. Y. Tang, Y. Lu, L. Yang, and H. Luo, Hyperspectral Image Classification Based on Regularized Sparse Representation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2174-2182, 2014.
16.H. Yuan, Y. Y. Tang, Y. Lu, L. Yang, and H. Luo, Spectral-Spatial Classification of Hyperspectral Image Based on Discriminant Analysis, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2035-2043, 2014.
17.H. Yuanand Y. Y. Tang, A Novel Sparsity-Based Framework Using Max Pooling Operation for Hyperspectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 8, pp. 3570-3576, 2014.
会议论文
1. J. Li,H. Yuan, L. L. Lai, H. Zheng, W. Qian, X. Zhou, Graph-Based Sparse Matrix Regression for 2D Feature Selection, International Conference on Wavelet Analysis and Pattern Recognition, 2018.
2. J. Zheng,H. Yuan, L. L. Lai, H. Zheng, Z. Wang, F. Wang, SGL-RFS: Semi-Supervised Graph Learning Robust Feature Selection, International Conference on Wavelet Analysis and Pattern Recognition, 2018.
3. J. Li,H. Yuan, L. L. Lai, Y. Cheung, Joint Collaborative Representation and Discriminative Projection for Pattern Classification, International Conference on Computational Intelligence and Security, 2018
4. Y. Tao,H. Yuan, and L. L. Lai, Nuclear Norm Joint Sparse Representation for Hyperspectral Image Classification, Chinese Automation Congress, 2017.
5.H. Yuan, J. Zheng, et al, A Generalized Discriminative Least Squares Regression Model, 4th Asian Conference on Pattern Recognition, 2017.
6.H. Yuan, Y. Y. Tang, et al, Feature Extraction Based on Kernel Sparse Representation for Hyperspectral Image Classification, IEEE International Conference on Systems, Man and Cybernetics, 2014, pp. 4210-4215.
7.H. Yuanand Y. Y. Tang, Multi-Scale Tensor L1-Based Algorithm for Hyperspectral Image Classification, IEEE International Conference on Pattern Recognition, 2014, pp. 1383-1388.
8.H. Yuan, Y. Lu, L. Yang, H. Luo, and Y. Y. Tang, Spectral-Spatial Linear Discriminant Analysis for Hyperspectral Image Classification, IEEE International Conference on Cybernetics, 2013, pp. 144-149.
9.H. Yuan, Y. Lu, L. Yang, H. Luo, and Y. Y. Tang, Sparse Representation Using Contextual Information for Hyperspectral Image Classification, IEEE International Conference on Cybernetics, 2013, pp. 138-143.
10. X. Lu,H. Yuan, P. Yan, Y. Yuan, and X. Li, Geometry constrained sparse coding for single image super-resolution, IEEE International Conference on Computer Vision and Pattern Recognition, 2012, pp. 1648-1655. (CCF A类会议)
11. X. Lu,H. Yuan, Y. Yuan, P. Yan, L. Li, and X. Li, Local learning-based image super-resolution, IEEE International Workshop on Multimedia Signal Processing, 2011, pp. 1-5.




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