Large-Scale Multi-View Subspace Clustering in Linear Time

  • Zhao Kang University of Electronic Science and Technology of China
  • Wangtao Zhou University of Electronic Science and Technology of China
  • Zhitong Zhao University of Electronic Science and Technology of China
  • Junming Shao University of Electronic Science and Technology of China
  • Meng Han University of Electronic Science and Technology of China
  • Zenglin Xu University of Electronic Science and Technology of China

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.

Published
2020-04-03
Section
AAAI Technical Track: Machine Learning