Post nonlinear independent subspace analysis

Authors:
Zoltán Szabó;Barnabás Póczos;Gábor Szirtes;András Lörincz
Affiliations:
Department of Information Systems, Eötvös Loránd University, Budapest, Hungary;Department of Information Systems, Eötvös Loránd University, Budapest, Hungary;Department of Information Systems, Eötvös Loránd University, Budapest, Hungary;Department of Information Systems, Eötvös Loránd University, Budapest, Hungary
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

In this paper a generalization of Post Nonlinear Independent Component Analysis (PNL-ICA) to Post Nonlinear Independent Subspace Analysis (PNL-ISA) is presented. In this framework sources to be identified can be multidimensional as well. For this generalization we prove a separability theorem: the ambiguities of this problem are essentially the same as for the linear Independent Subspace Analysis (ISA). By applying this result we derive an algorithm using the mirror structure of the mixing system. Numerical simulations are presented to illustrate the efficiency of the algorithm.