Auto-weighted multi-view clustering with the use of an augmented view

Abstract

Multi-view clustering is a powerful technique that leverages both consensus and complementary information from multiple perspectives to achieve impressive results. However, existing methods have not fully explored the inherent structural information contained in multi-view data, such as inadequately harnessing the consistency among views or neglecting to explore their high-order correlations. To address these limitations, we propose a novel auto-weighted multi-view clustering (AWMVC) method with the use of an augmented view. The main motivation of AWMVC is that each view naturally embodies consensus information, and concatenating the views maximizes the consistency properties. AWMVC first connects multiple views via feature concatenation to form a newly augmented view. Then, we stack the self-representation matrices of the original views and the newly augmented view, i.e., the concatenated view into a 3rd-order tensor, subject to a low-rank tensor constraint. Additionally, this approach automatically assigns weights to each view and constructs a more reliable affinity matrix. Our method unifies the feature concatenation process, high-order information, and a weighted strategy into one model, and is efficiently optimized through an iterative algorithm. Experimental results on benchmark datasets demonstrate that AWMVC outperforms state-of-the-art methods, highlighting the effectiveness of our approach.