Weighted feature selection via discriminative sparse multi-view learning

The matrix-based structured sparsity-inducing multi-view feature selection has received much attention because it can select the relevant features through the information-rich multi-view data instead of the single-view data. In this paper, a novel supervised sparse multi-view feature selection model is proposed based on the separable weighted loss term and the discriminative regularization terms. The proposed model adopts the separable strategy to enforce the weighted penalty for each view instead of using the concatenated feature vectors to calculate the penalty. Therefore, the proposed model is established by considering both the complementarity of multiple views and the specificity of each view. The derived model can be split into several small-scale problems in the process of optimization, and be solved efficiently via an iterative algorithm with low complexity. Furthermore, the convergence of the proposed iterative algorithm is investigated from both theoretical and experimental aspects. The extensive experiments compared with several state-of-the-art matrix-based feature selection methods on the widely used multi-view datasets show the effectiveness of the proposed method.