DocumentCode
2229306
Title
An Algorithm for Learning Principal Curves with Principal Component Analysis and Back-Propagation Network
Author
Wang, Y.H. ; Guo, Y. ; Fu, Y.C. ; Shen, Z.Y.
Author_Institution
Soochow Univ., Suzhou
fYear
2007
fDate
20-24 Oct. 2007
Firstpage
447
Lastpage
453
Abstract
A new algorithm for learning principal curves with definite mathematical representations is proposed based on combining the principal component analysis (PCA) and back-propagation (BP) network. The algorithm successfully turns an unsupervised learning problem into a supervised one by projecting a data set to its first component line and identifying the relation between the data points and their corresponding projection indices with BP network. This algorithm has been proved distinctly superior to the HS algorithm.
Keywords
backpropagation; neural nets; principal component analysis; unsupervised learning; back-propagation network; mathematical representations; principal component analysis; principal curves learning; unsupervised learning problem; Algorithm design and analysis; Application software; Backpropagation algorithms; Computer science; Data analysis; Intelligent networks; Intelligent systems; Principal component analysis; System analysis and design; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems Design and Applications, 2007. ISDA 2007. Seventh International Conference on
Conference_Location
Rio de Janeiro
Print_ISBN
978-0-7695-2976-9
Type
conf
DOI
10.1109/ISDA.2007.128
Filename
4389649
Link To Document