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Inhaltsangabe
A data mining and feature extraction technique called Signal Fraction Analysis (SFA) is introduced. The method is applicable to high dimensional data. The row-energy and column-energy optimization problems for signal-to-signal ratios are investigated. A generalized singular value problem is presented. This setting is distinguished from the Singular Value Decomposition (SVD). Two new generalized SVD type problems for computing subspace representations is introduced. A connection between SFA and Canonical Correlation Analysis is maintained. We implement and investigate a nonlinear extension to SFA based on a kernel method, i.e., Kernel SFA. We include a detailed derivation of the methodology using kernel principal component analysis as a prototype. These methods are compared using toy examples and the benefits of KSFA are illustrated. The book studies the applications of the proposed techniques in the brain EEG data analysis and beam- forming in wireless communication systems.
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A data mining and feature extraction technique called Signal Fraction Analysis (SFA) is introduced. The method is applicable to high dimensional data. The row-energy and column-energy optimization problems for signal-to-signal ratios are investigated. A generalized singular value problem is presented. This setting is distinguished from the Singular Value Decomposition (SVD). Two new generalized SVD type problems for computing subspace representations is introduced. A connection between SFA and Canonical Correlation Analysis is maintained. We implement and investigate a nonlinear extension to SFA based on a kernel method, i.e., Kernel SFA. We include a detailed derivation of the methodology using kernel principal component analysis as a prototype. These methods are compared using toy examples and the benefits of KSFA are illustrated. The book studies the applications of the proposed techniques in the brain EEG data analysis and beam- forming in wireless communication systems.
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High Dimensional Data Analysis
Verlag:
VDM Verlag Dr. Müller|VDM Verlag Dr. Müller e.K., 2008
ISBN 10: 3639074211
ISBN 13: 9783639074215
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Softcover
Anbieter: moluna, Greven, Deutschland
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Zustand: New. A data mining and feature extraction techniquecalled Signal Fraction Analysis (SFA) is introduced.The method is applicable to high dimensional data.The row-energy and column-energy optimizationproblems for signal-to-signal ratios areinvestigated.A generaliz. Artikel-Nr. 4954992
Anzahl: Mehr als 20 verfügbar
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High Dimensional Data Analysis : Overview, Analysis, and Applications
Verlag:
VDM Verlag Dr. Müller, VDM Verlag Dr. Müller E.K., 2008
ISBN 10: 3639074211
ISBN 13: 9783639074215
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: Neu. Neuware - A data mining and feature extraction techniquecalled Signal Fraction Analysis (SFA) is introduced.The method is applicable to high dimensional data.The row-energy and column-energy optimizationproblems for signal-to-signal ratios areinvestigated.A generalized singular value problem is presented.This setting is distinguished from the SingularValue Decomposition (SVD).Two new generalized SVD type problems for computingsubspace representations is introduced. A connectionbetween SFA and Canonical Correlation Analysis ismaintained. We implement and investigate a nonlinearextension to SFA based on a kernel method, i.e.,Kernel SFA.We include a detailed derivation of the methodologyusing kernel principal component analysis as aprototype. These methods are compared using toyexamples and the benefits of KSFA are illustrated.The book studies the applications of the proposedtechniques in the brain EEG data analysis and beam-forming in wireless communication systems. Artikel-Nr. 9783639074215
Anzahl: 2 verfügbar