LABIN: Balanced Min Cut for Large-Scale Data

Xiaojun Chen, Renjie Chen, Qingyao Wu, Yixiang Fang, Feiping Nie, Joshua Zhexue Huang

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Although many spectral clustering algorithms have been proposed during the past decades, they are not scalable to large-scale data due to their high computational complexities. In this paper, we propose a novel spectral clustering method for large-scale data, namely, large-scale balanced min cut (LABIN). A new model is proposed to extend the self-balanced min-cut (SBMC) model with the anchor-based strategy and a fast spectral rotation with linear time complexity is proposed to solve the new model. Extensive experimental results show the superior performance of our proposed method in comparison with the state-of-the-art methods including SBMC.

Original languageEnglish
Article number8712566
Pages (from-to)725-736
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume31
Issue number3
DOIs
StatePublished - Mar 2020

Keywords

  • Clustering
  • graph cut
  • large-scale data
  • spectral clustering

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