Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement

TL;DR

This paper introduces a novel multi-view clustering method that combines deep matrix factorization with global graph refinement, effectively capturing both shared and local features across views to improve clustering performance.

Contribution

It proposes a new deep semi-NMF based multi-view clustering approach with global graph refinement, addressing limitations of shallow models and view-specific feature neglect.

Findings

Outperforms existing methods on six benchmark datasets.

Effectively captures both shared and view-specific features.

Demonstrates convergence and stability of the optimization algorithm.

Abstract

Multi-view clustering is an important yet challenging task in machine learning and data mining community. One popular strategy for multi-view clustering is matrix factorization which could explore useful feature representations at lower-dimensional space and therefore alleviate dimension curse. However, there are two major drawbacks in the existing work: i) most matrix factorization methods are limited to shadow depth, which leads to the inability to fully discover the rich hidden information of original data. Few deep matrix factorization methods provide a basis for the selection of the new representation's dimensions of different layers. ii) the majority of current approaches only concentrate on the view-shared information and ignore the specific local features in different views. To tackle the above issues, we propose a novel Multi-View Clustering method with Deep semi-NMF and Global Graph Refinement (MVC-DMF-GGR) in this paper. Firstly, we capture new representation matrices for each view by hierarchical decomposition, then learn a common graph by approximating a combination of graphs which are reconstructed from these new representations to refine the new representations in return. An alternate algorithm with proved convergence is then developed to solve the optimization problem and the results on six multi-view benchmarks demonstrate the effectiveness and superiority of our proposed algorithm.