杜兰,
西安电子科技大学雷达信号处理国家重点实验室 西安 710071
基金项目:国家自然科学基金(61771362)，高等学校学科创新引智计划(B18039)，陕西省重点科技创新团队计划
详细信息
作者简介:张维：男，1992年生，博士生，研究方向为机器学习及其在雷达目标识别方面的应用
杜兰：女，1980年生，教授，博士生导师，研究方向为统计信号处理、雷达信号处理、机器学习及其在雷达目标检测与识别方面的应用
通讯作者:杜兰 dulan@mail.xidian.edu.cn
中图分类号:TN957.51计量
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被引次数:0
出版历程
收稿日期:2020-01-19
修回日期:2020-11-12
网络出版日期:2020-11-18
刊出日期:2021-05-18
An Ensembling One-class Classification Method Based on Beta Process Max-margin One-class Classifier
Wei ZHANG,Lan DU,
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Funds:The National Natural Science Foundation of China (61771362), The 111 Project (B18039), Shaanxi Innovation Team Project
摘要
摘要:一类分类是一种将目标类样本和其他所有的非目标类样本区分开的分类方法。传统的一类分类方法针对所有训练样本建立一个分类器，忽视了数据的内在结构，在样本分布复杂时，其分类性能会严重下降。为了提升复杂分布情况下的分类性能，该文提出一种集成式Beta过程最大间隔一类方法。该方法利用Dirichlet过程混合模型(DPM)对训练样本聚类，同时在每一个聚类学习一个Beta过程最大间隔一类分类器。通过多个分类器的集成，可以构造出一个描述能力更强的分类器，提升复杂分布下的分类效果。DPM聚类模型和Beta过程最大间隔一类分类器在同一个贝叶斯框架下联合优化，保证了每一个聚类样本的可分性。此外，在Beta过程最大间隔一类分类器中，加入了服从Beta过程先验分布的特征选择因子，从而可以降低特征冗余度以及提升分类效果。基于仿真数据、公共数据集和实测SAR图像数据的实验结果证明了所提方法的有效性。
关键词:雷达信号处理/
一类分类/
Dirichlet过程/
Beta过程
Abstract:In the problem of one-class classification, One-Class Classifier (OCC) tries to identify samples of a specific class, called the target class, among samples of all other classes. Traditional one-class classification methods design a classifier using all training samples and ignore the underlying structure of the data, thus their classification performance will be seriously degraded when dealing with complex distributed data. To overcome this problem, an ensembling one-class classification method based on Beta process max-margin one-class classifier is proposed in this paper. In the method, the input data is partitioned into several clusters with the Dirichlet Process Mixture (DPM), and a Beta Process Max-Margin One-Class Classifier (BPMMOCC) is learned in each cluster. With the ensemble of some simple classifiers, the complex nonlinear classification can be implemented to enhance the classification performance. Specifically, the DPM and BPMMOCC are jointly learned in a unified Bayesian frame to guarantee the separability in each cluster. Moreover, in BPMMOCC, a feature selection factor, which obeys the prior distribution of Beta process, is added to reduce feature redundancy and improve classification results. Experimental results based on synthetic data, benchmark datasets and Synthetic Aperture Radar (SAR) real data demonstrate the effectiveness of the proposed method.
Key words:Radar signal processing/
One-Class Classification (OCC)/
Dirichlet process/
Beta process
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