Nonnegative tucker decomposition with alpha-divergence

Yong Deok Kim, Andrzej Cichocki, Seungjin Choi

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

30 Citations (Scopus)

Abstract

Nonnegative Tucker decomposition (NTD) is a recent multiway extension of nonnegative matrix factorization (NMF), where nonnega-tivity constraints are incorporated into Tucker model. In this paper we consider α-divergence as a discrepancy measure and derive multiplicative updating algorithms for NTD. The proposed multiplicative algorithm includes some existing NMF and NTD algorithms as its special cases, since α-divergence is a one-parameter family of divergences which accommodates KL-divergence, Hellinger divergence, χ2 divergence, and so on. Numerical experiments on face images show how different values of α affect the factorization results under different types of noise.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages1829-1832
Number of pages4
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: 31 Mar 20084 Apr 2008

Publication series

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

Conference

Conference2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Country/TerritoryUnited States
CityLas Vegas, NV
Period31/03/084/04/08

Keywords

  • α-divergence
  • Nonnegative matrix factorization
  • Tensor factorization
  • Tucker models

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