Portada del libro de High Dimensional Data Analysis
Título del libro:

High Dimensional Data Analysis

Overview, Analysis, and Applications

VDM Verlag Dr. Müller (2008-10-09 )

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ISBN-13:

978-3-639-07421-5

ISBN-10:
3639074211
EAN:
9783639074215
Idioma del libro:
Inglés
Notas y citas / Texto breve:
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.
Editorial:
VDM Verlag Dr. Müller
Sitio web:
http://www.vdm-verlag.de
Por (autor):
Emdad Fatemeh
Número de páginas:
148
Publicado en:
2008-10-09
Stock:
Disponible
Categoría:
Matemáticas
Precio:
59.00 €
Palabras clave:
Data Mining, Feature Extraction, noise removal, classification, Pattern Analysis, Subspace methods, Principal Component Analysis, Maximum noise fraction.

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