Feature Extraction base on Local Maximum Margin Criterion

Maximum Margin Criterion (MMC) based Feature Extraction method is more efficient than LDA for calculating the discriminant vectors since it does not need to calculate the inverse within-class scatter matrix. However, MMC ignores the discriminative information within the local structures of samples. In this paper, we develop a novel criterion to address the issue, namely Local Maximum Margin Criterion (Local MMC). We define the total laplacian matrix, within-class laplacian matrix and between-class laplacian matrix using the samples similar weighting. Local MMC gets the discriminant vectors by maximizing the difference between between-class laplacian matrix and within-class laplacian matrix. Experiments on FERET face database show the effectiveness of the proposed Local MMC based feature extraction method.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Wankou Yang, Jianguo Wang, Mingwu Ren, Jingyu Yang
• Keywords: Between-class Laplacian Matrix, Computer Vision, ICPR 2008, Laplacian Matrix, Within-class Laplacian Matrix