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Principal component analysis network...

Kong, Xiangyu.

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Principal component analysis networks and algorithms

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Principal component analysis networks and algorithms/ by Xiangyu Kong, Changhua Hu, Zhansheng Duan.
作者:	Kong, Xiangyu.
其他作者:	Hu, Changhua.
出版者:	Singapore :Springer Singapore : : 2017.,
面頁冊數:	xxii, 323 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Principal components analysis. -
電子資源:	http://dx.doi.org/10.1007/978-981-10-2915-8
ISBN:	9789811029158

Principal component analysis networks and algorithms
Kong, Xiangyu.

Principal component analysis networks and algorithms[electronic resource] /by Xiangyu Kong, Changhua Hu, Zhansheng Duan. - Singapore :Springer Singapore :2017. - xxii, 323 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789811029158

Standard No.: 10.1007/978-981-10-2915-8doiSubjects--Topical Terms:

565921
Principal components analysis.

LC Class. No.: QA278.5

Dewey Class. No.: 519.5354

Principal component analysis networks and algorithms
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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.
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.
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