Semi-parametric Exponential Family PCA

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 dimensional, 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 favorable comparison to other related schemes both in terms of separating different populations and generalization to unseen samples.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Sajama, Alon Orlitsky
• Keywords: Count Data, Latent Distribution, Latent Variable Models, NIPS 2004, NIPS 2007