Multi-View Clustering With the Cooperation of Visible and Hidden Views

Zhaohong Deng, Ruixiu Liu, Peng Xu, Kup Sze Choi, Wei Zhang, Xiaobin Tian, Te Zhang, Ling Liang, Bin Qin, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Multi-view data are becoming common in real-world applications and many multi-view clustering algorithms have thus been proposed. The existing algorithms usually focus on the cooperation of different visible views in the original space but neglect the influence of the hidden information among these visible views, or they only consider the hidden information among the views. The algorithms are therefore not efficient since the available information is not fully exploited, particularly the otherness information in different views and the consistency information among them. In practice, the otherness and consistency information in multi-view data are both very useful for effective clustering analyses. In this study, a Multi-View clustering algorithm with the Cooperation of Visible and Hidden views, i.e., MV-Co-VH, is proposed. The MV-Co-VH algorithm first projects the multiple views from different visible spaces to the common hidden space by using non-negative matrix factorization to obtain the common hidden view data. Collaborative learning is then implemented in the clustering procedure based on the visible views and the shared hidden view. The experimental results of extensive experiments on UCI multi-view datasets and real-world image multi-view datasets show that the clustering performance of the proposed algorithm is competitive with or even better than that of the existing algorithms.

Original languageEnglish
Pages (from-to)803-815
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume34
Issue number2
DOIs
Publication statusPublished - 1 Feb 2022

Keywords

  • cooperation of visible and hidden views
  • Multi-view clustering
  • non-negative matrix factorization

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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