Scalable Multi-view Clustering with Graph Filtering

TL;DR

This paper introduces a scalable multi-view clustering framework that leverages graph filtering to enhance clustering quality by integrating feature and topology information, outperforming existing methods.

Contribution

It proposes a novel graph filtering-based framework for multi-view clustering that effectively combines attribute and graph data, addressing scalability and noise issues.

Findings

Outperforms state-of-the-art multi-view clustering methods

Effectively filters noise to produce smoother representations

Demonstrates scalability on large attribute and graph datasets

Abstract

With the explosive growth of multi-source data, multi-view clustering has attracted great attention in recent years. Most existing multi-view methods operate in raw feature space and heavily depend on the quality of original feature representation. Moreover, they are often designed for feature data and ignore the rich topology structure information. Accordingly, in this paper, we propose a generic framework to cluster both attribute and graph data with heterogeneous features. It is capable of exploring the interplay between feature and structure. Specifically, we first adopt graph filtering technique to eliminate high-frequency noise to achieve a clustering-friendly smooth representation. To handle the scalability challenge, we develop a novel sampling strategy to improve the quality of anchors. Extensive experiments on attribute and graph benchmarks demonstrate the superiority of our approach with respect to state-of-the-art approaches.