Sparse robust multiview feature selection via adaptive-weighting strategy

Due to the rich and comprehensive information of multiview data, multi-view learning has been attracted widely attention. Efficiently exploiting multiview data to select discriminative features to improve classification performance is very important in multi-view learning. Most existing supervised methods learn an entire projection matrix by concatenating multiple views into a long vector, thus they often ignore the relationship between views. To solve this problem, in this paper, we propose a novel sparse robust multiview feature selection model, which simultaneously considers the robustness, individuality and commonality of views via adaptive-weighting strategy. The model adopts the soft capped-norm loss to calculate the residual in each view to effectively reduce the impact of noises and outliers. Moreover, the model employs the adaptive-weighting strategy to show the individuality and commonality of views without introducing extra parameters. In addition, it introduces structured sparsity regularization to select the discriminative features. An efficient iterative algorithm is proposed to individually learn each block of the projection matrix with low computational complexity, and the convergence of the proposed optimization algorithm is verified theoretically and experimentally. The comparative experiments are conducted on multiview datasets with several state-of-the-art algorithms, and the experimental results show that the proposed method gets better performance than the others.