Novel hierarchical ALS algorithm for nonnegative tensor factorization

Anh Huy Phan, Andrzej Cichocki, Kiyotoshi Matsuoka, Jianting Cao

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

2 Citations (Scopus)

Abstract

The multiplicative algorithms are well-known for nonnegative matrix and tensor factorizations. The ALS algorithm for canonical decomposition (CP) has been proved as a "workhorse" algorithm for general multiway data. Unfortunately, for CP with nonnegativity constraints, this algorithm with a rectifier (projection) may not converge to the desired solution without additional regularization parameters in matrix inverses. The hierarchical ALS algorithm improves the performance of the ALS algorithm, outperforms the multiplicative algorithm. However, NTF algorithms can face problem with collinear or bias data. In this paper, we propose a novel algorithm which overwhelmingly outperforms all the multiplicative, and (H)ALS algorithms. By solving the nonnegative quadratic programming problems, a general algorithm of the HALS has been derived and experimentally confirmed its validity and high performance for normal and difficult benchmarks, and for real-world EEG dataset.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages1984-1987
Number of pages4
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

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

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • ALS
  • canonical polyadic decomposition (CP)
  • NMF
  • nonnegative quadratic programming
  • nonnegative tensor factorization

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