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A loss function approach to model selection in nonlinear principal components

✍ Scribed by AndrewR. Webb

Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
162 KB
Volume
12
Category
Article
ISSN
0893-6080

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✦ Synopsis

The nonlinear transformation of the input variables that characterises the first nonlinear principal component is modelled as a linear sum of radially-symmetric kernel functions. It is shown that the parameters of the variance maximising transformation may be obtained through the minimisation of a loss function measuring departure from homogeneity. An alternating least squares algorithm is given. This is used as the basis of a cross-validation routine for model selection.

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