Multi-graph Fusion for Multi-view Spectral Clustering

TL;DR

This paper introduces a novel multi-view spectral clustering method that simultaneously fuses multiple graphs and learns explicit cluster structures, addressing limitations of previous approaches that either average views or treat graph learning separately.

Contribution

It proposes a new multi-view spectral clustering model that integrates graph fusion and spectral clustering into a unified framework, capturing heterogeneous information more effectively.

Findings

Outperforms existing multi-view clustering methods on four datasets.

Effectively fuses heterogeneous view information into a single graph.

Produces more accurate and reliable clustering results.

Abstract

A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and demonstrated promising results recently. Despite progress, there are still two fundamental questions that stay unanswered to date. First, how to fuse different views into one graph. More often than not, the similarities between samples may be manifested differently by different views. Many existing algorithms either simply take the average of multiple views or just learn a common graph. These simple approaches fail to consider the flexible local manifold structures of all views. Hence, the rich heterogeneous information is not fully exploited. Second, how to learn the explicit cluster structure. Most existing methods don't pay attention to the quality of the graphs and perform graph learning and spectral clustering separately. Those unreliable graphs might lead to suboptimal clustering results. To fill these gaps, in this paper, we propose a novel multi-view spectral clustering model which performs graph fusion and spectral clustering simultaneously. The fusion graph approximates the original graph of each individual view but maintains an explicit cluster structure. Experiments on four widely used data sets confirm the superiority of the proposed method.