Independent Process Analysis without A Priori Dimensional Information

Details Independent Process Analysis without A Priori Dimensional Information Independent Process Analysis without A Priori Dimensional Information

2007-03-20

arxiv.org/abs/math/0703612v1

ICA-2007, pages 252-259

Mathematics

Statistics Theory

9 pages, 2 figures

Scientific paper

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 a particular application, we search for subspaces of facial components.

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