Nonnegative matrix factorization with local similarity learning

Chong Peng, Zhilu Zhang, Zhao Kang, Chenglizhao Chen, Qiang Cheng

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

Existing nonnegative matrix factorization methods usually focus on learning global structure of the data to construct basis and coefficient matrices, which ignores the local structure that commonly exists among data. To overcome this drawback, in this paper, we propose a new type of nonnegative matrix factorization method, which learns local similarity and clustering in a mutually enhanced way. The learned new representation is more representative in that it better reveals inherent geometric property of the data. Moreover, the new representation is performed in the kernel space, which enhances the capability of the proposed model in discovering nonlinear structures of data. Multiplicative updating rules are developed with theoretical convergence guarantees. Extensive experimental results have confirmed the effectiveness of the proposed model.

Original languageEnglish
Pages (from-to)325-346
Number of pages22
JournalInformation Sciences
Volume562
DOIs
StatePublished - Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Inc.

Keywords

  • Clustering
  • Local similarity
  • Nonnegative matrix factorization

ASJC Scopus subject areas

  • Software
  • Information Systems and Management
  • Artificial Intelligence
  • Theoretical Computer Science
  • Control and Systems Engineering
  • Computer Science Applications

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