Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering

TL;DR

This paper introduces a unified multi-view clustering method that jointly learns self-representation and cluster indicators, effectively capturing subspace structures across multiple data sources to improve clustering performance.

Contribution

It proposes a novel unified model that integrates self-representation learning with cluster indicator estimation for multi-view clustering.

Findings

Outperforms existing multi-view clustering methods on benchmark datasets

Effectively captures subspace structures across multiple views

Demonstrates superior clustering accuracy

Abstract

Multi-view subspace clustering aims to divide a set of multisource data into several groups according to their underlying subspace structure. Although the spectral clustering based methods achieve promotion in multi-view clustering, their utility is limited by the separate learning manner in which affinity matrix construction and cluster indicator estimation are isolated. In this paper, we propose to jointly learn the self-representation, continue and discrete cluster indicators in an unified model. Our model can explore the subspace structure of each view and fusion them to facilitate clustering simultaneously. Experimental results on two benchmark datasets demonstrate that our method outperforms other existing competitive multi-view clustering methods.