Data-dependent kernel discriminant analysis for feature extraction and classification

Subspace analysis is an effective technique for feature extraction, which aims at finding a low-dimensional space of high-dimensional data. In this paper, a novel subspace analysis method based on Data-Dependent Kernel Discriminant Analysis (DDKDA) is proposed for dimension reduction. The procedure of DDKDA contains two stages: one is to And the optimal combination coefficients by solving a constrained optimization function which transformed to an eigenvalue problem; other is to implement KDA under the optimal datadependent kernel with Fisher criterion. DDKDA is more adaptive to the input data than KDA owing to the optimization of projection from input space to feature space with the datadependent kernel, which enhances the performance of KDA. Experiments on the ORL and Yale face databases demonstrate the good performance of the proposed algorithm.