A binary feature selection framework in kernel spaces

In this paper, we propose a binary feature selection framework in kernel spaces, where each feature is projected into kernel spaces and a binary classification task is constructed in this space. Subsequently, the features are selected according to the normal vector of the learned classifier, which reflects the importance of each feature. To achieve the effect of feature selection, an £i-norm regularization is imposed on the normal vector to enforce its sparsity. Also, our framework can be naturally extended to the semi-supervised feature selection scenario via the well-known manifold regularization technique. Furthermore, the issue of eliminating the potential redundancy among the selected features is well discussed. Finally, we provide some theoretical results which guarantee the feasibility of the proposed framework. Comprehensive experiments have been conducted on six benchmark data sets and the results demonstrate the performance of our framework.