title: A loss function approach to model selection in nonlinear principal components.
abstract: 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.
authors.affiliation: Defence Evaluation and Research Agency, St Andrews Road, Malvern, UK
authors.forename: Andrew R.
authors.initials: AR
authors.lastname: Webb