PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Autoregressive independent process analysis with missing observations
Zoltan Szabo
In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 28-30 Apr 2010, Bruges, Belgium.

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Abstract

The goal of this paper is to search for independent multidimensional processes subject to missing and mixed observations. The corresponding cocktail-party problem has a number of successful applications, however, the case of missing observations has been worked out only for the simplest Independent Component Analysis (ICA) task, where the hidden processes (i) are one-dimensional, and (ii) signal generation in time is independent and identically distributed (i.i.d.). Here, the missing observation situation is extended to processes with (i) autoregressive (AR) dynamics and (ii) multidimensional driving sources. Performance of the solution method is illustrated by numerical examples.

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EPrint Type:Conference or Workshop Item (Paper)
Additional Information:http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2010-52.pdf
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9539
Deposited By:Zoltan Szabo
Deposited On:30 May 2012

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