Group non-convex sparsity regularized partially shared dictionary learning for multi-view learning

Multi-view learning aims to obtain more comprehensive understanding than single-view learning by observing objects from different views. However, most existing multi-view learning algorithms are still facing problems in obtaining enough discriminative information from the multi-view data: (1) most models cannot fully exploit consistent and complementary information simultaneously; (2) existing group sparsity based multi-view learning methods cannot extract the most relevant and sparest features. This paper proposes the efficient group non-convex sparsity regularized partially shared dictionary learning for multi-view learning, which employs the partially shared dictionary learning model to excavate both consistency and complementarity simultaneously from the multi-view data, and utilizes the generalized group non-convex sparsity for more discriminative and sparser representations beyond the convex l(2 ,1) norm. To solve the non-convex optimization problem, we derive the generalized optimization framework for different group non-convex sparsity regularizers based on the proximal splitting method. Corresponding proximal operators for structured sparse coding in the framework are derived to form algorithms for different group non-convex sparsity regularizers, i.e., the l(2, p) (0 < p < 1) norm and the l(2, log) regularizer. In experiments, we conduct multi-view clustering in seven real-world multi-view datasets, and performances validate the effectiveness of both group information and non-convexity. Furthermore, results show that appropriate coefficient sharing ratios can help to exploit consistent information while keeping complementary information from multi-view data, thus helping to improve clustering performances. In addition, the convergence performances show that the proposed algorithm can obtain the best clustering performances among compared algorithms and can converge efficiently and stably with reasonable running time costs. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an efficient group non-convex sparsity regularized partially shared dictionary learning method for multi-view learning. The method utilizes the partially shared dictionary learning model to extract both consistency and complementarity from multi-view data and employs generalized group non-convex sparsity for discriminative and sparse representations. Experimental results validate the effectiveness of both group information and non-convexity, and demonstrate that appropriate coefficient sharing ratios can improve clustering performances. The proposed algorithm outperforms compared algorithms in terms of convergence performance and has reasonable running time costs.