PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Undercomplete blind subspace deconvolution via linear prediction
Zoltan Szabo, Barnabas Poczos and András Lorincz
In: 18th European Conference on Machine Learning (ECML), 17-21 Sep 2007, Warsaw, Poland.

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Abstract

We present a novel solution technique for the blind subspace deconvolution (BSSD) problem, where temporal convolution of multidimensional hidden independent components is observed and the task is to uncover the hidden components using the observation only. We carry out this task for the undercomplete case (uBSSD): we reduce the original uBSSD task via linear prediction to independent subspace analysis (ISA), which we can solve. As it has been shown recently, applying temporal concatenation can also reduce uBSSD to ISA, but the associated ISA problem can easily become 'high dimensional' [1]. The new reduction method circumvents this dimensionality problem. We perform detailed studies on the efficiency of the proposed technique by means of numerical simulations. We have found several advantages: our method can achieve high quality estimations for smaller number of samples and it can cope with deeper temporal convolutions.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:http://dx.doi.org/10.1007/978-3-540-74958-5_75
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8372
Deposited By:Zoltan Szabo
Deposited On:01 December 2011

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