Principal component analysis networks and algorithms

This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various a...

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Bibliographic Details
Main Authors: Kong, Xiangyu, (Author), Hu, Changhua, (Author), Duan, Zhansheng, (Author)
Format: eBook
Language: English
Published: Singapore : Springer, [2017]
Subjects:
Principal components analysis.
elektronické knihy
electronic books
ISBN: 9789811029158
9789811029134
Physical Description: 1 online resource

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024 7 |a 10.1007/978-981-10-2915-8  |2 doi 
035 |a (OCoLC)968211913  |z (OCoLC)969446267  |z (OCoLC)974651013  |z (OCoLC)981884354  |z (OCoLC)1005793175  |z (OCoLC)1011999229  |z (OCoLC)1048135829  |z (OCoLC)1058409749  |z (OCoLC)1066553366  |z (OCoLC)1086513225  |z (OCoLC)1112588130  |z (OCoLC)1112838432  |z (OCoLC)1113083528  |z (OCoLC)1116975591  |z (OCoLC)1122818133 
100 1 |a Kong, Xiangyu,  |e author. 
245 1 0 |a Principal component analysis networks and algorithms /  |c Xiangyu Kong, Changhua Hu, Zhansheng Duan. 
264 1 |a Singapore :  |b Springer,  |c [2017] 
300 |a 1 online resource 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references. 
505 0 |a Introduction -- Eigenvalue and singular value decomposition -- Principal component analysis neural networks -- Minor component analysis neural networks -- Dual purpose methods for principal and minor component analysis -- Deterministic discrete time system for PCA or MCA methods -- Generalized feature extraction method -- Coupled principal component analysis -- Singular feature extraction neural networks. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields. 
590 |a SpringerLink  |b Springer Complete eBooks 
650 0 |a Principal components analysis. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Hu, Changhua,  |e author. 
700 1 |a Duan, Zhansheng,  |e author. 
776 0 8 |i Printed edition:  |z 9789811029134 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://link.springer.com/10.1007/978-981-10-2915-8  |y Plný text 
992 |c NTK-SpringerENG 
999 |c 99718  |d 99718 
993 |x NEPOSILAT  |y EIZ 

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