Multi-Channel Subspace Mapping Using an Information Maximization Criterion

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dc.contributor.author Al-Ani, A
dc.contributor.author Deriche, M
dc.date.accessioned 2010-05-28T09:47:51Z
dc.date.issued 2004-01
dc.identifier.citation Multidimensional Systems and Signal Processing, 2004, 15 (2), pp. 117 - 145
dc.identifier.issn 0923-6082
dc.identifier.other C1UNSUBMIT 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.
dc.publisher Springer Netherlands
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon 10.1023/B:MULT.0000017022.18495.d5
dc.rights The original publication is available at www.springerlink.com en_US
dc.subject subspace mapping - multi-channel signal processing - Hybrid Information Maximization (HIM) - Principal Component Analysis (PCA) - Canonical Correlation Analysis (CCA, Industrial Engineering & Automation
dc.subject subspace mapping - multi-channel signal processing - Hybrid Information Maximization (HIM) - Principal Component Analysis (PCA) - Canonical Correlation Analysis (CCA; Industrial Engineering & Automation
dc.title Multi-Channel Subspace Mapping Using an Information Maximization Criterion
dc.type Journal Article
dc.parent Multidimensional Systems and Signal Processing
dc.journal.volume 2
dc.journal.volume 15
dc.journal.number 2 en_US
dc.publocation Dordrecht, The Netherlands en_US
dc.identifier.startpage 117 en_US
dc.identifier.endpage 145 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 090609 Signal Processing
dc.personcode 040052 en_US
dc.personcode 0000030077 en_US
dc.percentage 100 en_US
dc.classification.name Signal Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords subspace mapping - multi-channel signal processing - Hybrid Information Maximization (HIM) - Principal Component Analysis (PCA) - Canonical Correlation Analysis (CCA en_US
dc.description.keywords subspace mapping - multi-channel signal processing - Hybrid Information Maximization (HIM) - Principal Component Analysis (PCA) - Canonical Correlation Analysis (CCA
pubs.embargo.period Not known
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 Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies


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