Double Graphs Regularized Multi-view Subspace Clustering

TL;DR

This paper introduces a novel multi-view subspace clustering method that leverages both global and local data structures through double graph regularization, improving clustering performance on real-world datasets.

Contribution

The proposed DGRMSC method uniquely integrates global and local structural information via double graph regularization in a unified framework.

Findings

Outperforms existing multi-view clustering methods on real datasets.

Effectively captures both global and local data structures.

Demonstrates robustness and improved clustering accuracy.

Abstract

Recent years have witnessed a growing academic interest in multi-view subspace clustering. In this paper, we propose a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to harness both global and local structural information of multi-view data in a unified framework. Specifically, DGRMSC firstly learns a latent representation to exploit the global complementary information of multiple views. Based on the learned latent representation, we learn a self-representation to explore its global cluster structure. Further, Double Graphs Regularization (DGR) is performed on both latent representation and self-representation to take advantage of their local manifold structures simultaneously. Then, we design an iterative algorithm to solve the optimization problem effectively. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method.