Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering

TL;DR

This paper introduces DMFAW, a deep matrix factorization method with adaptive feature weights for multi-view clustering, improving stability, convergence, and clustering accuracy through feature selection and dynamic weight adjustment.

Contribution

The paper proposes a novel deep matrix factorization model with adaptive weights controlled by a control theory inspired mechanism for enhanced multi-view clustering.

Findings

DMFAW outperforms state-of-the-art methods on benchmark datasets.

The adaptive weight mechanism improves model stability and convergence.

Feature selection enhances clustering quality.

Abstract

Recently, deep matrix factorization has been established as a powerful model for unsupervised tasks, achieving promising results, especially for multi-view clustering. However, existing methods often lack effective feature selection mechanisms and rely on empirical hyperparameter selection. To address these issues, we introduce a novel Deep Matrix Factorization with Adaptive Weights for Multi-View Clustering (DMFAW). Our method simultaneously incorporates feature selection and generates local partitions, enhancing clustering results. Notably, the features weights are controlled and adjusted by a parameter that is dynamically updated using Control Theory inspired mechanism, which not only improves the model's stability and adaptability to diverse datasets but also accelerates convergence. A late fusion approach is then proposed to align the weighted local partitions with the consensus partition. Finally, the optimization problem is solved via an alternating optimization algorithm with theoretically guaranteed convergence. Extensive experiments on benchmark datasets highlight that DMFAW outperforms state-of-the-art methods in terms of clustering performance.