Discriminant similarity and variance preserving projection for feature extraction

作者：Huang Pu^*; Chen Caikou; Tang Zhenmin; Yang Zhangjing

来源：Neurocomputing, 2014, 139: 180-188.

DOI：10.1016/j.neucom.2014.02.047

• Dimensionality reduction
• Manifold-based learning
• Intrinsic graph
• Penalty graph
• Variance

In this paper, a novel supervised dimensionality reduction algorithm called discriminant similarity and variance preserving projection (DSVPP) is presented for feature extraction and recognition. More specifically, we redefine the intrinsic graph and penalty graph to model the intra-class compactness and inter-class separability of data points, where the intrinsic graph characterizes the similarity information of the same-class points and the penalty graph characterizes the variance information of the not-same-class points. Using the two graphs, the within-class scatter and the between-class scatter are computed, and then a concise feature extraction criterion is raised via minimizing the difference between them. Experimental results on the Wine data set, ORL, FERET and AR face databases show the effectiveness of the proposed method.

• 出版日期2014-9-2
• 单位南京理工大学; 扬州大学