Seeking an appropriate alternative least squares algorithm for nonnegative tensor factorizations: A novel recursive solution for nonnegative quadratic programming and NTF

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6 Citations (Scopus)

Abstract

Alternative least squares (ALS) algorithm is considered as a "work-horse" algorithm for general tensor factorizations. A common form of this algorithm for nonnegative tensor factorizations (NTF) is always combined with a nonlinear projection (rectifier) to enforce nonnegative entries during the estimation. Such simple modification often provides acceptable results for general data. However, this does not establish an appropriate ALS algorithm for NTF. This kind of ALS algorithm often converges slowly, or cannot converge to the desired solution, especially for collinear data. To this end, in this paper, we reinvestigate the nonnegative quadratic programming, propose a recursive method for solving this problem. Then, we formulate a novel ALS algorithm for NTF. The validity and high performance of the proposed algorithm has been confirmed for difficult benchmarks, and also in an application of object classification, and analysis of EEG signals.

Original languageEnglish
Pages (from-to)623-637
Number of pages15
JournalNeural Computing and Applications
Volume21
Issue number4
DOIs
Publication statusPublished - Jun 2012
Externally publishedYes

Keywords

  • ALS
  • Canonical polyadic decomposition (CP)
  • Clustering
  • EEG analysis
  • NMF
  • Nonnegative quadratic programming
  • Nonnegative tensor factorization
  • Object classification
  • PARAFAC
  • Parallel computing

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