Simultaneous Robust Matching Pursuit for Multi-view Learning

Joint sparse representation (JSR) has attracted massive attention with many successful applications in pattern recognition recently. In this paper, we propose a novel robust multi-view JSR method referred to as Simultaneous Robust Matching Pursuit (SRMP) based on the outlier-resistant M-estimator originating from robust statistics. Because of the complexity of the objective function, we design an efficient optimization algorithm to implement SRMP based on the half-quadratic theory. In addition, we have also extended the proposed method for the problems of multi-view subspace clustering and multi-view pattern classification, respectively. The experimental results corroborate the efficacy and robustness of SRMP for multi-view data recovery, subspace clustering and classification.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a novel robust multi-view JSR method called Simultaneous Robust Matching Pursuit (SRMP) and applies it to multi-view data recovery, subspace clustering, and classification problems. Experimental results demonstrate the effectiveness and robustness of the method in multiple application scenarios.