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..

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

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.

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/10.1007/978-3-540-74494-8_32
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

Available Versions of this Item