Subspace Learning of Neural Networks

by Jian Cheng Lv, Zhang Yi, Jiliu Zhou

Overview

Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The authors summarize a decade of high quality research offering a host of practical applications. They demonstrate ways to extend the use of algorithms to fields such as encryption communication, data mining, computer vision, and signal and image processing to name just a few. The brilliance of the work lies with how it coherently builds a theoretical understanding of the convergence behavior of subspace learning algorithms through a summary of chaotic behaviors.

Product Details

ISBN-13:	9781351834018
Publisher:	CRC Press
Publication date:	09/03/2018
Series:	Automation and Control Engineering , #42
Sold by:	Barnes & Noble
Format:	eBook
Pages:	248
File size:	8 MB

About the Author

Jian Cheng LV and Zhang Yi are affiliated with the Machine Intelligence Lab of the College of Computer Science at Sichuan University. Jiliu Zhou is affiliated with the College of Computer Science at Sichuan University.

Table of Contents

Introduction. PCA Learning Algorithms with Constants Learning Rates. PCA Learning Algorithms with Adaptive Learning Rates. GHA PCA Learning Algorithms. MCA Learning Algorithms. ICA Learning Algorithms. Chaotic Behaviors Arising from Learning Algorithms. Multi-Block-Based MCA for Nonlinear Surface Fitting. A ICA Algorithm for Extracting Fetal Electrocardiogram. Some Applications of PCA Neural Networks. Conclusion.