LAUSR

In applications, using features obtained from multiple views to describe objects has become popular because multiple views contain much more information than the single view. As the dimensions of the data sets are high, which may cause expensive time consumption and memory space, how to identify the representative views and features becomes a crucial problem. Multi-view feature selection that can integrate multiple views to select important and relevant features to improve performance has attracted more and more attentions in recent years. Previous supervised multi-view feature selection methods usually establish the models by concatenating multiple views into long vectors. However, this concatenation is not physically meaningful and implies that different views play the similar roles for specific tasks. In this paper, we propose a novel supervised multi-view feature selection method based on the weighted hinge loss (WHMVFS) that can learn the corresponding weight for each view and implement sparsity from the group and individual point of views under the structured sparsity framework. The newly proposed multi-view weighted hinge loss penalty not only has the ability to select more discriminative features for classification, but also can make the involved optimization problem be decomposed into several small scale subproblems, which can be easily solved by an iterative algorithm, and the convergence of the iterative algorithm is also proved. Experimental results conducted on real-world data sets show the effectiveness of the proposed method.