Exploratory Data Analysis Using Radial Basis Function Latent Variable Models

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Alan Marrs, Andrew Webb


Two developments of nonlinear latent variable models based on radial basis functions are discussed: in the first, the use of priors or constraints on allowable models is considered as a means of preserving data structure in low-dimensional representations for visualisation purposes. Also, a resampling approach is introduced which makes more effective use of the latent samples in evaluating the likelihood.