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)
Corporate Author: SpringerLink (Online service)
Format: e-Book
Language:English
Published: Singapore : Springer Nature Singapore : Imprint: Springer, 2017.
Edition:1st ed. 2017.
Subjects:
Computational intelligence.
Pattern recognition systems.
Neural networks (Computer science) .
Statistics .
Algorithms.
Signal processing.
Computational Intelligence.
Automated Pattern Recognition.
Mathematical Models of Cognitive Processes and Neural Networks.
Statistical Theory and Methods.
Signal, Speech and Image Processing .
Online Access:Full-text access
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Table of Contents:
  • 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.

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