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Novel Multi-layer Non-negative Tensor Factorization with Sparsity Constraints

  • Andrzej Cichocki
  • Rafal Zdunek
  • Seungjin Choi
  • Robert Plemmons
  • Shun-ichi Amari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4432)

Abstract

In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust in the presence of noise and has many potential applications, including multi-way blind source separation (BSS), multi-sensory or multi-dimensional data analysis, and sparse image coding. We consider alpha- and beta-divergences as error (cost) functions and derive three different algorithms: (1) multiplicative updating; (2) fixed point alternating least squares (FPALS); (3) alternating interior-point gradient (AIPG) algorithm. We also incorporate these algorithms into multilayer networks. Experimental results confirm the very useful behavior of our multilayer 3D NTF algorithms with multi-start initializations.

Keywords

Monte Carlo Blind Source Separation Nonnegative Matrix Factorization Alternate Little Square Parallel Factor Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Andrzej Cichocki
    • 1
  • Rafal Zdunek
    • 1
  • Seungjin Choi
    • 2
  • Robert Plemmons
    • 3
  • Shun-ichi Amari
    • 1
  1. 1.RIKEN Brain Science Institute, Wako-shiJapan
  2. 2.POSTECHKorea
  3. 3.Dept. of Mathematics and Computer Science, Wake Forest UniversityUSA

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