Multiview partitioning via tensor methods

Xinhai Liu, Shuiwang Ji, Wolfgang Glänzel, Bart De Moor

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

Clustering by integrating multiview representations has become a crucial issue for knowledge discovery in heterogeneous environments. However, most prior approaches assume that the multiple representations share the same dimension, limiting their applicability to homogeneous environments. In this paper, we present a novel tensor-based framework for integrating heterogeneous multiview data in the context of spectral clustering. Our framework includes two novel formulations; that is multiview clustering based on the integration of the Frobenius-norm objective function (MC-FR-OI) and that based on matrix integration in the Frobenius-norm objective function (MC-FR-MI). We show that the solutions for both formulations can be computed by tensor decompositions. We evaluated our methods on synthetic data and two real-world data sets in comparison with baseline methods. Experimental results demonstrate that the proposed formulations are effective in integrating multiview data in heterogeneous environments.

Original languageEnglish
Article number6193101
Pages (from-to)1056-1069
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume25
Issue number5
DOIs
Publication statusPublished - Apr 8 2013

Keywords

  • Multiview clustering
  • higher order orthogonal iteration
  • multilinear singular value decomposition
  • spectral clustering
  • tensor decomposition

ASJC Scopus subject areas

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

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