Scalable Multi-view Subspace Clustering with Unified Anchors

Mengjing Sun, Pei Zhang, Siwei Wang, Sihang Zhou, Wenxuan Tu, Xinwang Liu, En Zhu, Changjian Wang

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

172 Scopus citations

Abstract

Multi-view subspace clustering has received widespread attention to effectively fuse multi-view information among multimedia applications. Considering that most existing approaches' cubic time complexity makes it challenging to apply to realistic large-scale scenarios, some researchers have addressed this challenge by sampling anchor points to capture distributions in different views. However, the separation of the heuristic sampling and clustering process leads to weak discriminate anchor points. Moreover, the complementary multi-view information has not been well utilized since the graphs are constructed independently by the anchors from the corresponding views. To address these issues, we propose a Scalable Multi-view Subspace Clustering with Unified Anchors (SMVSC). To be specific, we combine anchor learning and graph construction into a unified optimization framework. Therefore, the learned anchors can represent the actual latent data distribution more accurately, leading to a more discriminative clustering structure. Most importantly, the linear time complexity of our proposed algorithm allows the multi-view subspace clustering approach to be applied to large-scale data. Then, we design a four-step alternative optimization algorithm with proven convergence. Compared with state-of-the-art multi-view subspace clustering methods and large-scale oriented methods, the experimental results on several datasets demonstrate that our SMVSC method achieves comparable or better clustering performance much more efficiently. The code of SMVSC is available at https://github.com/Jeaninezpp/SMVSC.

Original languageEnglish
Title of host publicationMM 2021 - Proceedings of the 29th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages3528-3536
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

This work was supported by the National Key R&D Program of China 2020AAA0107100, the Natural Science Foundation of China (project no. 61872377, 61922088, 61773392, 61976196 and 62006237).

Keywords

  • multi-view clustering
  • scalable graph clustering
  • subspace clustering

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