Multi-view clustering for multiple manifold learning via concept factorization

Abstract

Clustering of multi-view data has attracted great interest in machine learning. Multi-view data is based on complementary and consistent information due to the collection of numerous sources. However, the current multi-view clustering method does not focus on learning the manifold structure for consensus representation over the kernel space, the overfitting redundant features of diverse view data, and ignoring the correlation among these complementary multiple views. In this article, a novel multi-view clustering approach is introduced to tackle this issue, which deploys concept factorization to indulge the intrinsic data information. The intrinsic geometry of the data and consensus representation are preserved through the utilization of manifold learning. In addition, the correlation constraint is adopted for learning the common shared structure from diverse views, and the smooth regularization term is deployed to alleviate the overfitting of views. Finally, we fuse all individual terms to formulate the objective function and design a theoretically guaranteed optimization procedure to solve the model. Extensive experiments conducted on the benchmark datasets suggest that the proposed algorithm surpasses state-of-the-art approaches regarding clustering performance.