Independent process analysis without a priori dimensional information

Authors:
Barnabás Póczos;Zoltán Szabó;Melinda Kiszlinger;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:
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Year:
2007

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Abstract

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.