A Comparative Study of Linear and Nonlinear Feature Extraction Methods

	This Article
	Subscribers, please Login Purchase article: $19 PDF IEEE Xplore Subscribers RSS feed
	Share
	Email this Article to a friend
	Bibliographic References
	ASCII Text BibTex RefWorks Procite/RefMan/Endnote
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Cheong Hee Park Articles by Haesun Park Articles by Panos Pardalos

Fourth IEEE International Conference on Data Mining (ICDM'04)

Brighton, United Kingdom

November 01-November 04

ISBN: 0-7695-2142-8

	ASCII Text	x
	Cheong Hee Park, Haesun Park, Panos Pardalos, "A Comparative Study of Linear and Nonlinear Feature Extraction Methods," Data Mining, IEEE International Conference on, pp. 495-498, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10066, author = {Cheong Hee Park and Haesun Park and Panos Pardalos}, title = {A Comparative Study of Linear and Nonlinear Feature Extraction Methods}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {495-498}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10066}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - A Comparative Study of Linear and Nonlinear Feature Extraction Methods SN - 0-7695-2142-8 SP495 EP498 A1 - Cheong Hee Park, A1 - Haesun Park, A1 - Panos Pardalos, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Cheong Hee Park, University of Minnesota, Minneapolis

Haesun Park, University of Minnesota, Minneapolis

Panos Pardalos, University of Florida, Gainesville

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10066

This paper presents theoretical relationships among several generalized LDA algorithms and proposes computationally efficient approaches for them utilizing the relationships. Generalized LDA algorithms are extended nonlinearly by kernel methods resulting in nonlinear discriminant analysis. Performances and computational complexities of these linear and nonlinear discriminant analysis algorithms are compared.

Citation:

Cheong Hee Park, Haesun Park, Panos Pardalos, "A Comparative Study of Linear and Nonlinear Feature Extraction Methods," icdm, pp.495-498, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004

Peer Review Notice | Give Us Feedback

Usage of this product signifies your acceptance of the Terms of Use.