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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Main Authors: Kong, Xiangyu, Hu, Changhua (Author), Duan, Zhansheng (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Singapore Springer Singapore 2017, 2017
Edition:1st ed. 2017
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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245 0 0 |a Principal Component Analysis Networks and Algorithms  |h Elektronische Ressource  |c by Xiangyu Kong, Changhua Hu, Zhansheng Duan 
250 |a 1st ed. 2017 
260 |a Singapore  |b Springer Singapore  |c 2017, 2017 
300 |a XXII, 323 p. 86 illus., 41 illus. in color  |b online resource 
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 
653 |a Mathematical statistics 
653 |a Mathematical Models of Cognitive Processes and Neural Networks 
653 |a Engineering 
653 |a Statistical Theory and Methods 
653 |a Optical pattern recognition 
653 |a Computational Intelligence 
653 |a Signal, Image and Speech Processing 
653 |a Computer software 
653 |a Pattern Recognition 
653 |a Algorithm Analysis and Problem Complexity 
700 1 |a Hu, Changhua  |e [author] 
700 1 |a Duan, Zhansheng  |e [author] 
710 2 |a SpringerLink (Online service) 
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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