Feature Extraction base on Local Maximum Margin Criterion
Wankou Yang, Jianguo Wang, Mingwu Ren, Jingyu Yang · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
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.