Locality Relationship Constrained Multi-view Clustering Framework

TL;DR

This paper introduces LRC-MCF, a novel multi-view clustering framework that leverages locality relationships and low-rank constraints to improve clustering accuracy across diverse datasets.

Contribution

The paper proposes a new multi-view clustering method that effectively captures locality and similarity relationships, incorporating view weights and low-rank constraints for better performance.

Findings

Outperforms existing methods on seven benchmark datasets.

Effectively captures locality and similarity relationships among views.

Reduces redundancy in learned representations.

Abstract

In most practical applications, it's common to utilize multiple features from different views to represent one object. Among these works, multi-view subspace-based clustering has gained extensive attention from many researchers, which aims to provide clustering solutions to multi-view data. However, most existing methods fail to take full use of the locality geometric structure and similarity relationship among samples under the multi-view scenario. To solve these issues, we propose a novel multi-view learning method with locality relationship constraint to explore the problem of multi-view clustering, called Locality Relationship Constrained Multi-view Clustering Framework (LRC-MCF). LRC-MCF aims to explore the diversity, geometric, consensus and complementary information among different views, by capturing the locality relationship information and the common similarity relationships among multiple views. Moreover, LRC-MCF takes sufficient consideration to weights of different views in finding the common-view locality structure and straightforwardly produce the final clusters. To effectually reduce the redundancy of the learned representations, the low-rank constraint on the common similarity matrix is considered additionally. To solve the minimization problem of LRC-MCF, an Alternating Direction Minimization (ADM) method is provided to iteratively calculate all variables LRC-MCF. Extensive experimental results on seven benchmark multi-view datasets validate the effectiveness of the LRC-MCF method.