Semi-supervised sparse feature selection based on multi-view Hessian regularization

Semi-supervised sparse feature selection has received increasing attention in recent years. However, most of the semi-supervised feature selection algorithms are developed for the single-view data and cannot naturally handle data represented by multi-view features. Moreover, most existing semi-supervised sparse feature selection methods are based on Laplacian regularization, which is lack of extrapolating power. Therefore, we present a new semi-supervised sparse feature selection framework based on multi-view Hessian regularization to obtain better performance in this paper. A simple yet efficient iterative method is proposed to solve the objective function. We apply the proposed method into image annotation task and conduct extensive experiments on two web image datasets. Experimental results show that the proposed method can realize feature selection well.