Multi-Channel Subspace Mapping Using an Information Maximization Criterion

Al-Ani, A; Deriche, M

Multi-Channel Subspace Mapping Using an Information Maximization Criterion

Al-Ani, A

Deriche, M

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Publication Type:: Journal Article
Citation:: Multidimensional Systems and Signal Processing, 2004, 15 (2)
Issue Date:: 2004-01-01

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Field	Value	Language
dc.contributor.author	Al-Ani, A https://orcid.org/0000-0002-8092-8954	en_US
dc.contributor.author	Deriche, M	en_US
dc.date.issued	2004-01-01	en_US
dc.identifier.citation	Multidimensional Systems and Signal Processing, 2004, 15 (2)	en_US
dc.identifier.issn	0923-6082	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9159
dc.description.abstract	A new hybrid information maximization (HIM) algorithm is derived. This algorithm is able to perform subspace mapping of multi-channel signals, where the input (feature) vector for each of the channels is linearly transformed to an output vector. The algorithm is based on maximizing the mutual information (MI) between input and output sets for each of the channels, and between output sets across channels. Such formulation leads to a substantial redundancy reduction in the output sets, and the extraction of higher order features that exhibit coherence across time and/or space. In this paper, we develop the proposed algorithm and show that it combines efficiently the strengths of two well-known subspace mapping techniques, namely the principal component analysis (PCA) and the canonical correlation analysis (CCA). Unlike CCA, which is limited to two channels, the HIM algorithm can easily be extended to multiple channels. A number of simulations and real experiments are conducted to compare the performance of HIM to that of PCA and CCA.	en_US
dc.relation.ispartof	Multidimensional Systems and Signal Processing	en_US
dc.relation.isbasedon	10.1023/B:MULT.0000017022.18495.d5	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Multi-Channel Subspace Mapping Using an Information Maximization Criterion	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	15	en_US
utslib.for	090609 Signal Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

A new hybrid information maximization (HIM) algorithm is derived. This algorithm is able to perform subspace mapping of multi-channel signals, where the input (feature) vector for each of the channels is linearly transformed to an output vector. The algorithm is based on maximizing the mutual information (MI) between input and output sets for each of the channels, and between output sets across channels. Such formulation leads to a substantial redundancy reduction in the output sets, and the extraction of higher order features that exhibit coherence across time and/or space. In this paper, we develop the proposed algorithm and show that it combines efficiently the strengths of two well-known subspace mapping techniques, namely the principal component analysis (PCA) and the canonical correlation analysis (CCA). Unlike CCA, which is limited to two channels, the HIM algorithm can easily be extended to multiple channels. A number of simulations and real experiments are conducted to compare the performance of HIM to that of PCA and CCA.

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http://hdl.handle.net/10453/9159