Hierarchical Representation for Multi-view Clustering: From Intra-sample to Intra-view to Inter-view

Jing Hua Yang, Chuan Chen*, Hong Ning Dai, Meng Ding, Le Le Fu, Zibin Zheng

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

2 Citations (Scopus)

Abstract

Multi-view clustering (MVC) aims at exploiting the consistent features within different views to divide samples into different clusters. Existing subspace-based MVC algorithms usually assume linear subspace structures and two-stage similarity matrix construction strategies, thereby posing challenges in imprecise low-dimensional subspace representation and inadequacy of exploring consistency. This paper presents a novel hierarchical representation for MVC method via the integration of intra-sample, intra-view, and inter-view representation learning models. In particular, we first adopt the deep autoencoder to adaptively map the original high-dimensional data into the latent low-dimensional representation of each sample. Second, we use the self-expression of the latent representation to explore the global similarity between samples of each view and obtain the subspace representation coefficients. Third, we construct the third-order tensor by arranging multiple subspace representation matrices and impose the tensor low-rank constraint to sufficiently explore the consistency among views. Being incorporated into a unified framework, these three models boost each other to achieve a satisfactory clustering result. Moreover, an alternating direction method of multipliers algorithm is developed to solve the challenging optimization problem. Extensive experiments on both simulated and real-world multi-view datasets show the superiority of the proposed method over eight state-of-the-art baselines.

Original languageEnglish
Title of host publicationCIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
PublisherAssociation for Computing Machinery (ACM)
Pages2362-2371
Number of pages10
ISBN (Print)9781450392365
DOIs
Publication statusPublished - 17 Oct 2022
Event31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States
Duration: 17 Oct 202221 Oct 2022
https://dl.acm.org/doi/proceedings/10.1145/3511808

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Country/TerritoryUnited States
CityAtlanta
Period17/10/2221/10/22
Internet address

Scopus Subject Areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

User-Defined Keywords

  • alternating direction method of multipliers
  • deep autoencoder
  • hierarchical representation
  • multi-view clustering
  • tensor low-rank constraint

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