Learning probabilistic kernel feature subspace with side-information for classification

Kernel PCA is an efficient method for nonlinear feature extraction. We address two issues in kernel PCA based feature extraction and classification. First, it extracts features without utilizing sample label information. Second, it does not provide a practical means to choose the dimensionality for principal subspace. In this paper, one kind of side-information is incorporated into kernel PCA to solve the first problem. And a complete probabilistic density function is estimated in kernel space so that the choice of dimensionality for principal subspace becomes less important. The proposed model is named probabilistic kernel feature subspace (PKFS). Experiments show that it achieves promising performance and outperforms many other algorithms in classification.