Multi-view spectral clustering via sparse graph learning

Zhanxuan Hu, Feiping Nie, Wei Chang, Shuzheng Hao, Rong Wang, Xuelong Li

Research output: Contribution to journalArticlepeer-review

114 Scopus citations

Abstract

Although numerous multi-view spectral clustering algorithms have been developed, most of them generally encounter the following two deficiencies. First, high time cost. Second, inferior operability. To this end, in this work we provide a simple yet effective method for multi-view spectral clustering. The main idea is to learn a consistent similarity matrix with sparse structure from multiple views. We show that proposed method is fast, straightforward to implement, and can achieve comparable or better clustering results compared to several state-of-the-art algorithms. Furthermore, the computation complexity of proposed method is approximately equivalent to the single-view spectral clustering. For these advantages, it can be considered as a baseline for multi-view spectral clustering.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalNeurocomputing
Volume384
DOIs
StatePublished - 7 Apr 2020

Keywords

  • Clustering
  • Multi-view
  • Sparse
  • Spectral clustering

Fingerprint

Dive into the research topics of 'Multi-view spectral clustering via sparse graph learning'. Together they form a unique fingerprint.

Cite this