Semi-parametric Exponential Family PCA

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

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Sajama Sajama, Alon Orlitsky


We present a semi-parametric latent variable model based technique for density modelling, dimensionality reduction and visualization. Unlike previous methods, we estimate the latent distribution non-parametrically which enables us to model data generated by an underlying low dimen- sional, multimodal distribution. In addition, we allow the components of latent variable models to be drawn from the exponential family which makes the method suitable for special data types, for example binary or count data. Simulations on real valued, binary and count data show fa- vorable comparison to other related schemes both in terms of separating different populations and generalization to unseen samples.