High Dimensional Data Analysis
Overview, Analysis, and Applications
978-3-639-07421-5
3639074211
148
2008-10-09
59,00 €
eng
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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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Matematika
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