A supervised multi-view feature selection method based on locally sparse regularization and block computing

With the increasing scale of obtained multi-view data, how to deal with large-scale multi view data quickly and efficiently is a significant problem. In this paper, a novel supervised multi-view feature selection method based on locally sparse regularization and block computing is proposed to solve the problem. Specifically, the multi-view dataset is firstly divided into sub-blocks according to classes and views. Then with the aid of the Alternating Direction Method of Multipliers (ADMM), a sharing sub-model is proposed to perform feature selection on each class by integrating each view's locally sparse regularizers and shared loss that makes all views share a common penalty and regresses samples to their labels. Finally, all the sharing sub-models are fused to form the final general additive feature selection model, in which each sub-block adjusts its corresponding variables to perform block-based feature selection. In the optimization process, the proposed model can be decomposed into multiple separate subproblems, and an efficient optimization algorithm is proposed to solve them quickly. The comparison experiments with several state-of-the-art feature selection methods show that the proposed method is superior in classification accuracy and training speed. CO 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper presents a novel supervised multi-view feature selection method based on locally sparse regularization and block computing. By dividing the multi-view dataset into sub-blocks and utilizing ADMM, a sharing sub-model is proposed for feature selection on each class. The proposed method outperforms several state-of-the-art feature selection methods in terms of classification accuracy and training speed.