Non-negative tensor factorization using alpha and beta divergences

Andrzej Cichocki, Rafal Zdunek, Seungjin Choi, Robert Plemmons, Shun Ichi Amari

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

76 Citations (Scopus)

Abstract

In this paper we propose new algorithms for 3D tensor decomposition/ factorization with many potential applications, especially in multi-way Blind Source Separation (BSS), multidimensional data analysis, and sparse signal/image representations. We derive and compare three classes of algorithms: Multiplicative, Fixed-Point Alternating Least Squares (FPALS) and Alternating Interior-Point Gradient (AIPG) algorithms. Some of the proposed algorithms are characterized by improved robustness, efficiency and convergence rates and can be applied for various distributions of data and additive noise.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesIII1393-III1396
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: 15 Apr 200720 Apr 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
ISSN (Print)1520-6149

Conference

Conference2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/TerritoryUnited States
CityHonolulu, HI
Period15/04/0720/04/07

Keywords

  • Feature extraction
  • Learning systems
  • Linear approximation
  • Optimization
  • Signal representations

Fingerprint

Dive into the research topics of 'Non-negative tensor factorization using alpha and beta divergences'. Together they form a unique fingerprint.

Cite this