Semi-supervised Dimensionality Reduction Based on Kernel Marginal Fisher Analysis and Sparsity Preserving

摘要：

　　Considering the limit that marginal fisher analysis(MFA)cant take advantage of the discriminant information in the training samples,this paper proposed a semi-supervised dimensionality reduction based on kernel marginal fisher analysis and sparsity preserving.The new algorithm firstly gets the sparse reconstruction of the samples.Secondly it uses the samples with labels to construct the intra-class similarity graph and inter-class penalty graph.Then the algorithm uses all of the samples to get the global information.At last,we make it nonlinearized.The algorithm takes advantage of the information in both the label samples and unlabel samples.Experiments with the proposed algorithm were conducted on YALE and ORL,our algorithm outperforms based on traditional dimensionality reduction algorithms with maximum average recognition rate by 2.48%and 4.88%respectively.

关键词： semi-supervised learning sparsity MFA face recognition

作者: XUE Wei WANG Zheng-qun LI Feng ZHOU Zhong-xia

作者单位: Department of Information and Engineering,Yang zhou university,Yangzhou 225127,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 4631-4635

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）