Sparsity-cognizant overlapping co-clustering for behavior inference in social networks

Hao Zhu, Gonzalo Mateos, Georgios B Giannakis, Nikolaos Sidiropoulos, Arindam Banerjee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Co-clustering can be viewed as a two-way (bilinear) factorization of a large data matrix into dense/uniform and possibly overlapping sub-matrix factors (co-clusters). This combinatorially complex problem emerges in several applications, including behavior inference tasks encountered with social networks. Existing co-clustering schemes do not exploit the fact that overlapping factors are often sparse, meaning that their dimension is considerably smaller than that of the data matrix. Based on plaid models which allow for overlapping submatrices, the present paper develops a sparsity-cognizant overlapping co-clustering (SOC) approach. Numerical tests demonstrate the ability of the novel SOC scheme to globally detect multiple overlapping co-clusters, outperforming the original plaid model algorithms which rely on greedy search and ignore sparsity.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages3534-3537
Number of pages4
DOIs
StatePublished - Nov 8 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period3/14/103/19/10

Keywords

  • Clustering
  • Overlapping co-clustering
  • Plaid models
  • Sparsity

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