Self-Representation Subspace Clustering for Incomplete Multi-view Data

Jiyuan Liu, Xinwang Liu, Yi Zhang, Pei Zhang, Wenxuan Tu, Siwei Wang, Sihang Zhou, Weixuan Liang, Siqi Wang, Yuexiang Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

70 Scopus citations

Abstract

Incomplete multi-view clustering is an important research topic in multimedia where partial data entries of one or more views are missing. Current subspace clustering approaches mostly employ matrix factorization on the observed feature matrices to address this issue. Meanwhile, self-representation technique is left unexplored, since it explicitly relies on full data entries to construct the coefficient matrix, which is contradictory to the incomplete data setting. However, it is widely observed that self-representation subspace method enjoys a better clustering performance over the factorization based one. Therefore, we adapt it to incomplete data by jointly performing data imputation and self-representation learning. To the best of our knowledge, this is the first attempt in incomplete multi-view clustering literature. Besides, the proposed method is carefully compared with current advances in experiment with respect to different missing ratios, verifying its effectiveness.

Original languageEnglish
Title of host publicationMM 2021 - Proceedings of the 29th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages2726-2734
Number of pages9
ISBN (Electronic)9781450386517
DOIs
StatePublished - Oct 17 2021
Externally publishedYes
Event29th ACM International Conference on Multimedia, MM 2021 - Virtual, Online, China
Duration: Oct 20 2021Oct 24 2021

Publication series

NameMM 2021 - Proceedings of the 29th ACM International Conference on Multimedia

Conference

Conference29th ACM International Conference on Multimedia, MM 2021
Country/TerritoryChina
CityVirtual, Online
Period10/20/2110/24/21

Funding

62006236), Education Ministry-China Mobile Research Funding (No. MCM20170404), Hunan Provincial Natural Science Foundation (No. 2020JJ5673) and NUDT Research Project (No. ZK20-10). The work is supported by National Key R&D Program of China (No. 2020AAA0107100), National Natural Science Foundation of China (No. 61922088, 61773392, 61872377, 61976196, 62006237 and

Keywords

  • incomplete data
  • multi-view clustering
  • self-representation learning
  • subspace clustering

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