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Autoregressive Independent Process Analysis with Missing Observations

Szabo, Z; (2010) Autoregressive Independent Process Analysis with Missing Observations. In: Proceedings of ESANN 2010: 18th European Symposium on Artificial Neural Networks. (pp. 159 - 164). D-Side Publications Green open access

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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.

Type: Proceedings paper
Title: Autoregressive Independent Process Analysis with Missing Observations
Event: ESANN 2010, 18th European Symposium on Artificial Neural Networks, 28-30 April 2010, Bruges, Belgium
Location: Bruges, Belgium
Dates: 2010-04-28 - 2010-04-30
ISBN: 2930307102
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.elen.ucl.ac.be/esann/proceedings/elect...
Language: English
Additional information: Proceedings paper no. ES2010-52. - This is the published version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: https://discovery.ucl.ac.uk/id/eprint/1433158
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