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Post Nonlinear Independent Subspace Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4668)

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.

Keywords

Independent Component Analysis Independent Component Analysis Iterate Function System Machine Learn Research Linear Mixture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  1. 1.Department of Information Systems, Eötvös Loránd University, Pázmány P. sétány 1/C, Budapest H-1117Hungary

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