Feature Extraction Using Laplacian Maximum Margin Criterion

Authors:
Wankou Yang;Changyin Sun;Helen S. Du;Jingyu Yang
Affiliations:
School of Automation, Southeast University, Nanjing, China 210096;School of Automation, Southeast University, Nanjing, China 210096;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China 210094
Venue:
Neural Processing Letters
Year:
2011

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Abstract

Feature extraction by Maximum Margin Criterion (MMC) can more efficiently calculate the discriminant vectors than LDA, by avoiding calculation of the inverse within-class scatter matrix. But MMC ignores the local structures of samples. In this paper, we develop a novel criterion to address this issue, namely Laplacian Maximum Margin Criterion (Laplacian MMC). We define the total Laplacian matrix, within-class Laplacian matrix and between-class Laplacian matrix by using the similar weight of samples to capture the scatter information. Laplacian MMC based feature extraction gets the discriminant vectors by maximizing the difference between between-class laplacian matrix and within-class laplacian matrix. Experiments on FERET and AR face databases show that Laplacian MMC works well.