Dimensionality reduction assisted tensor clustering

Yanfeng Sun, Junbin Gao, Xia Hong, Yi Guo, Chris J. Harris

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

7 Citations (Scopus)

Abstract

![CDATA[This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.]]
Original languageEnglish
Title of host publicationProceedings of the 2014 International Joint Conference on Neural Networks, July 6-11, 2014, Beijing, China
PublisherIEEE
Pages1565-1572
Number of pages8
ISBN (Print)9781479914845
DOIs
Publication statusPublished - 2014
EventInternational Joint Conference on Neural Networks -
Duration: 6 Jul 2014 → …

Conference

ConferenceInternational Joint Conference on Neural Networks
Period6/07/14 → …

Keywords

  • cluster analysis
  • clustering
  • dimension reduction (statistics)

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