PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Independent process analysis without a priori dimensional information
Barnabas Poczos, Zoltan Szabo, Melinda Kiszlinger and András Lorincz
In: 7th International Conference on Independent Component Analysis and Signal Separation (ICA), 9-12 Sep 2007, London, U.K..

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Recently, several algorithms have been proposed for independent subspace analysis where hidden variables are i.i.d. processes. We show that these methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our paper is that we introduce a cascade of algorithms, which aims to solve these tasks without previous knowledge about the number and the dimensions of the hidden processes. Our claim is supported by numerical simulations. As an illustrative application where the dimensions of the hidden variables are unknown, we search for subspaces of facial components.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8370
Deposited By:Zoltan Szabo
Deposited On:01 December 2011

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