Independent subspace analysis can cope with the 'curse of dimensionality'

We search for hidden independent components, in particular we consider the independent subspace analysis (ISA) task. Earlier ISA procedures assume that the dimensions of the components are known. Here we show a method that enables the non-combinatorial estimation of the components. We make use of a...

Teljes leírás

Elmentve itt :
Bibliográfiai részletek
Szerzők: Szabó Zoltán
Lőrincz András
Testületi szerző: Symposium of Young Scientists on Intelligent Systems (1.) (2006) (Budapest)
Dokumentumtípus: Cikk
Megjelent: 2007
Sorozat:Acta cybernetica 18 No. 2
Kulcsszavak:Számítástechnika, Kibernetika
Tárgyszavak:
Online Access:http://acta.bibl.u-szeged.hu/12812
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520 3 |a We search for hidden independent components, in particular we consider the independent subspace analysis (ISA) task. Earlier ISA procedures assume that the dimensions of the components are known. Here we show a method that enables the non-combinatorial estimation of the components. We make use of a decomposition principle called the ISA separation theorem. According to this separation theorem the ISA task can be reduced to the independent component analysis (ICA) task that assumes one-dimensional components and then to a grouping procedure that collects the respective non-independent elements into independent groups. We show that non-combinatorial grouping is feasible by means of the non-linear f-correlation matrices between the estimated components. 
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