Multi-view Clustering via Deep Matrix Factorization and Partition Alignment

TL;DR

This paper introduces a deep matrix decomposition approach for multi-view clustering that leverages partition alignment to better utilize view-specific and shared information, improving clustering performance.

Contribution

It proposes a novel deep matrix factorization framework with partition alignment to enhance multi-view clustering by exploiting view-specific structures and partition information.

Findings

Outperforms state-of-the-art methods on six benchmark datasets.

Effectively captures view-specific and shared information.

Proven convergence of the optimization algorithm.

Abstract

Multi-view clustering (MVC) has been extensively studied to collect multiple source information in recent years. One typical type of MVC methods is based on matrix factorization to effectively perform dimension reduction and clustering. However, the existing approaches can be further improved with following considerations: i) The current one-layer matrix factorization framework cannot fully exploit the useful data representations. ii) Most algorithms only focus on the shared information while ignore the view-specific structure leading to suboptimal solutions. iii) The partition level information has not been utilized in existing work. To solve the above issues, we propose a novel multi-view clustering algorithm via deep matrix decomposition and partition alignment. To be specific, the partition representations of each view are obtained through deep matrix decomposition, and then are jointly utilized with the optimal partition representation for fusing multi-view information. Finally, an alternating optimization algorithm is developed to solve the optimization problem with proven convergence. The comprehensive experimental results conducted on six benchmark multi-view datasets clearly demonstrates the effectiveness of the proposed algorithm against the SOTA methods.