MULTIDIMENSIONAL-SCALING BY ITERATIVE MAJORIZATION USING RADIAL BASIS FUNCTIONS
This paper considers the use of radial basis functions for modelling the non-linear transformation of a data set obtained by a multidimensional scaling analysis. This approach has two advantages over conventional nonmetric multidimensional scaling. It reduces the number of parameters to estimate and it provides a transformation that may be used on an unseen test set. A scheme based on iterative majorization is proposed for obtaining the parameters of the network.
iterative majorization radial basis functions multidimensional scaling pattern recognition nonlinear transformation feature extraction discriminant analysis nonlinear principal components analysis cross-validation