Feature extraction using Laplacian Maximum Margin Criterion

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.