Reference : Distortion function and clustering for local linear models

	Document type :	Scientific journals : Short communication
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Physics Engineering, computing & technology : Mechanical engineering Engineering, computing & technology : Electrical & electronics engineering
	To cite this reference:	http://hdl.handle.net/2268/19602

Title :	Distortion function and clustering for local linear models
Language :	English
Author, co-author :	Kerschen, Gaëtan [Université de Liège - ULg > Département d'aérospatiale et mécanique > Laboratoire de structures et systèmes spatiaux >]
	Yan, Ai Min [>Université de Liège - ULg > Département d'aérospatiale et mécanique > LTAS - Vibrations et identification des structures > > > >]
	Golinval, Jean-Claude [Université de Liège - ULg > Département d'aérospatiale et mécanique > LTAS - Vibrations et identification des structures >]
Publication date :	7-Feb-2005
Journal title :	Journal of Sound & Vibration
Publisher :	Academic Press Ltd Elsevier Science Ltd
Volume :	280
Issue/season :	1-2
Pages :	443-448
Audience :	International
ISSN :	0022-460X
City :	London
Keywords :	[en] Principal component analysis ; Distortion function ; clustering for local linear models
Abstract :	[en] Principal component analysis (PCA) is a ubiquitous statistical technique for data analysis. PCA is however limited by its linearity and may sometimes be too simple for dealing with real-world data especially when the relations among variables are nonlinear. Recent years have witnessed the emergence of nonlinear generalizations of PCA, as for instance nonlinear principal component analysis (NLPCA) [1] or vector quantization principal component analysis (VQPCA) [2]. VQPCA involves a two-step procedure, namely a clustering of the data space into several regions and the application of PCA in each local region. In Ref. [3], VQPCA was applied for the reconstruction of dynamical response and it was shown that it is potentially a more effective tool than conventional PCA. The purpose of this technical note is to further investigate VQPCA and to have a closer look at the choice of the distortion function used for clustering the data space.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/2268/19602
DOI :	10.1016/j.jsv.2004.02.043

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