PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Post nonlinear independent subspace analysis
Zoltan Szabo, Barnabas Poczos, Gabor Szirtes and András Lorincz
In: International Conference on Artificial Neural Networks (ICANN), 9-13 Sep 2007, Porto, Portugal.

There is a more recent version of this eprint available. Click here to view it.

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.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Additional Information:http://dx.doi.org/10.1007/978-3-540-74690-4_69
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8371
Deposited By:Zoltan Szabo
Deposited On:01 December 2011

Available Versions of this Item