Two Dimensional Slow Feature Discriminant Analysis via L 2,1 Norm Minimization for Feature Extraction

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via L2,1 norm minimization (2DSFDA-L2,1) is proposed. 2DSFDA-L2,1 integrates L2,1 norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, L2,1 norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed L2,1 nonlinear model into a linear regression type. Additionally, 2DSFDA-L2,1 is extended to a bilateral projection version called BSFDA-L2,1. The advantage of BSFDA-L2,1 is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed 2DSFDA-L2,1/BSFDA-L2,1 can obtain competitive performance. KCI Citation Count: 0