Fast non-linear dimension reduction

Nandakishore Kambhatla, Todd K. Leen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

This paper presents a new algorithm for nonlinear dimension reduction. The algorithm builds a piece-wise linear model of the data. This piece-wise linear model provides compression that is superior to the globally linear model produced by principal component analysis. On several examples the piece-wise linear model also provides compression that is superior to the global non-linear model constructed by a five-layer, autoassociative neural network. Furthermore, the new algorithm trains significantly faster than the autoassociative network.

Original languageEnglish (US)
Title of host publication1993 IEEE International Conference on Neural Networks, ICNN 1993
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1213-1218
Number of pages6
ISBN (Electronic)0780309995
DOIs
StatePublished - Jan 1 1993
EventIEEE International Conference on Neural Networks, ICNN 1993 - San Francisco, United States
Duration: Mar 28 1993Apr 1 1993

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume1993-January
ISSN (Print)1098-7576

Other

OtherIEEE International Conference on Neural Networks, ICNN 1993
CountryUnited States
CitySan Francisco
Period3/28/934/1/93

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ASJC Scopus subject areas

  • Software

Cite this

Kambhatla, N., & Leen, T. K. (1993). Fast non-linear dimension reduction. In 1993 IEEE International Conference on Neural Networks, ICNN 1993 (pp. 1213-1218). [298730] (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 1993-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICNN.1993.298730