Improved nonlinear PCA based on RBF networks and principal curves

Xueqin Liu, Kang Li, Marion McAfee, Jing Deng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Nonlinear PCA based on neural networks (NN) have been widely used in different applications in the past decade. There is a difficulty with the determination of the optimal topology for the networks that are used. Principal curves were introduced to nonlinear PCA to separate the original complex five-layer NN into two three-layer RBF networks and eased the above problem. Using the advantage of Fast Recursive Algorithm, where the number of neurons, the location of centers, and the weights between the hidden layer and the output layer can be identified simultaneously for the RBF networks, the topology problem for the nonlinear PCA based on NN can thus be solved. The simulation result shows that the method is excellent for solving nonlinear principal component problems.

Original languageEnglish
Title of host publicationLife System Modeling and Intelligent Computing - International Conference on LSMS 2010 and ICSEE 2010, Proceedings
Pages7-15
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2010
Event2010 International Conference on Life System Modeling and Simulation, LSMS 2010 and the 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010 - Wuxi, China
Duration: 17 Sep 201020 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6328 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2010 International Conference on Life System Modeling and Simulation, LSMS 2010 and the 2010 International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010
Country/TerritoryChina
CityWuxi
Period17/09/1020/09/10

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