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Titolo:
Topographic independent component analysis
Autore:
Hyvarinen, A; Hoyer, PO; Inki, M;
Indirizzi:
Helsinki Univ Technol, Neural Networks Res Ctr, FIN-02015 HUT, Finland Helsinki Univ Technol HUT Finland FIN-02015 Ctr, FIN-02015 HUT, Finland
Titolo Testata:
NEURAL COMPUTATION
fascicolo: 7, volume: 13, anno: 2001,
pagine: 1527 - 1558
SICI:
0899-7667(200107)13:7<1527:TICA>2.0.ZU;2-Z
Fonte:
ISI
Lingua:
ENG
Soggetto:
PRIMARY VISUAL-CORTEX; SELF-ORGANIZING MAP; BLIND SEPARATION; MAXIMUM-LIKELIHOOD; NEURAL NETWORKS; STRIATE CORTEX; NATURAL IMAGES; COMPLEX CELLS; SPARSE CODE; ALGORITHM;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Engineering, Computing & Technology
Citazioni:
44
Recensione:
Indirizzi per estratti:
Indirizzo: Hyvarinen, A Helsinki Univ Technol, Neural Networks Res Ctr, FIN-02015 HUT, Finland Helsinki Univ Technol HUT Finland FIN-02015 15 HUT, Finland
Citazione:
A. Hyvarinen et al., "Topographic independent component analysis", NEURAL COMP, 13(7), 2001, pp. 1527-1558

Abstract

In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated "independent" components are often not at all independent. We propose that this residual dependence structure could be used to define a topographic order for the components. In particular, a distance between two components could be defined using their higher-order correlations, and this distance could be used to create a topographic representation. Thus, we obtain a linear decomposition into approximately independent components, where the dependence of two components is approximated by the proximity of the components in the topographic representation.

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Documento generato il 20/01/20 alle ore 08:00:50