Large-scale Multi-view Subspace Clustering in Linear Time

TL;DR

This paper introduces a large-scale multi-view subspace clustering algorithm with linear time complexity, enabling efficient clustering on big data by leveraging anchor graphs and spectral clustering on smaller graphs.

Contribution

It presents a novel linear-time MVSC algorithm that uses anchor graphs to efficiently handle large-scale multi-view data, also applicable to single-view scenarios.

Findings

Outperforms state-of-the-art methods in large-scale datasets

Achieves linear time complexity in multi-view clustering

Demonstrates effectiveness on various benchmark datasets

Abstract

A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.