Flexible component analysis for sparse, smooth, nonnegative coding or representation

Andrzej Cichocki, Anh Huy Phan, Rafal Zdunek, Li Qing Zhang

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

21 Citations (Scopus)

Abstract

In the paper, we present a new approach to multi-way Blind Source Separation (BSS) and corresponding 3D tensor factorization that has many potential applications in neuroscience and multi-sensory or multidimensional data analysis, and neural sparse coding. We propose to use a set of local cost functions with flexible penalty and regularization terms whose simultaneous or sequential (one by one) minimization via a projected gradient technique leads to simple Hebbian-like local algorithms that work well not only for an over-determined case but also (under some weak conditions) for an under-determined case (i.e., a system which has less sensors than sources). The experimental results confirm the validity and high performance of the developed algorithms, especially with usage of the multi-layer hierarchical approach.

Original languageEnglish
Title of host publicationNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Pages811-820
Number of pages10
EditionPART 1
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
Duration: 13 Nov 200716 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4984 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Neural Information Processing, ICONIP 2007
Country/TerritoryJapan
CityKitakyushu
Period13/11/0716/11/07

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