Novel multi-layer non-negative tensor factorization with sparsity constraints

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

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

35 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdaptive and Natural Computing Algorithms - 8th International Conference, ICANNGA 2007, Proceedings
PublisherSpringer Verlag
Pages271-280
Number of pages10
EditionPART 2
ISBN (Print)9783540715900
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007 - Warsaw, Poland
Duration: 11 Apr 200714 Apr 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4432 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007
Country/TerritoryPoland
CityWarsaw
Period11/04/0714/04/07

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