Reference : Distortion function and clustering for local linear models
Scientific journals : Short communication
Physical, chemical, mathematical & earth Sciences : Physics
Engineering, computing & technology : Mechanical engineering
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/19602
Distortion function and clustering for local linear models
English
Kerschen, Gaëtan mailto [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 mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > LTAS - Vibrations et identification des structures >]
7-Feb-2005
Journal of Sound & Vibration
Academic Press Ltd Elsevier Science Ltd
280
1-2
443-448
Yes (verified by ORBi)
International
0022-460X
London
[en] Principal component analysis ; Distortion function ; clustering for local linear models
[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.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/19602
10.1016/j.jsv.2004.02.043

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