Robust weighted co-clustering with global and local discrimination

Zhoumin Lu, Shiping Wang, Genggeng Liu, Feiping Nie

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

In the past few decades, the clustering problem has made considerable progress, and co-clustering algorithms have attracted more attention. Compared with one-side clustering, co-clustering not only groups samples according to the distribution of features but also groups features according to the distribution of samples at the same time. This duality helps to explore the structural information of data, such as genes and texts. In this paper, a new co-clustering algorithm is proposed to simultaneously consider feature weights, data noise, local manifolds, and global scatter, named robust weighted co-clustering with global and local discrimination. Furthermore, an alternate update rule is put forward to optimize objective, theoretically proven to converge. Then, the algorithm's duality, robustness, and effectiveness have been verified on synthetic, corrupted, and real datasets, respectively. The runtime and parameter sensitivity of the algorithm are also analyzed. Finally, sufficient experiments clarify the competitiveness of our algorithm compared to other ones.

Original languageEnglish
Article number109405
JournalPattern Recognition
Volume138
DOIs
StatePublished - Jun 2023

Keywords

  • Co-clustering
  • Global discrimination
  • Local discrimination
  • Machine learning
  • Nonnegative matrix factorization

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