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

摘要

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 ℓ2,1 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 ℓ2,p (0