Principal Component Analysis Netorks and Algorithms [Paperback]

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.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

Xiangyu Kong, received the B.S. degree in optical engineering from Beijing Institute of Technology, China, in 1990, and Ph.D. degree in control engineering from Xi'an Jiaotong University, China, in 2005. He is currently an associate professor in department of control engineering at Xi'an Institute of Hi-Tech. His research interests include adaptive signal processing, neural networks and feature extraction. He has published two monographs (both as first author) and more than 60 papers, in whilóQ