In this book a number of novel algorithms for dimension reduction
and statistical pattern recognition for both supervised and
unsupervised learning tasks have been presented. Several existing
pattern classifiers and dimension reduction algorithms are studied.
Their limitations and/or weaknesses are considered and accordingly
improved techniques are given which overcome several of their
shortcomings. Highlights are: i) Survey of basic dimensional
reduction tools viz. principal component analysis and linear
discriminant analysis are conducted. ii) Development of Fast PCA
technique which finds the desired number of leading eigenvectors
with much less computational cost. iii) Development of gradient LDA
technique for SSS problem. iv) The rotational LDA technique is
developed to reduce the overlapping of samples between the classes.
v) A combined classifier using MDC, class-dependent PCA and LDA is
presented. vi) The splitting technique initialization is introduced
in the local PCA technique. vii) A new perspective of subspace ICA
(generalized ICA, where all the components need not be independent)
is introduced by developing vector kurtosis (an extension of
kurtosis) function.
General
Imprint: |
Lap Lambert Academic Publishing
|
Country of origin: |
Germany |
Release date: |
May 2012 |
First published: |
May 2012 |
Authors: |
Alok Sharma
|
Dimensions: |
229 x 152 x 13mm (L x W x T) |
Format: |
Paperback - Trade
|
Pages: |
220 |
ISBN-13: |
978-3-8465-3331-4 |
Categories: |
Books >
Computing & IT >
General theory of computing >
General
|
LSN: |
3-8465-3331-9 |
Barcode: |
9783846533314 |
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