Large-Scale Multi-View Subspace Clustering in Linear Time

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…

• NN
  National Natural Science Foundation of China

  Awards: ZYGX2017KYQD177, 61572111, 61806045
• FR
  Fundamental Research Funds for the Central Universities

  Awards: ZYGX2017KYQD177, 61572111