PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian Exponential Family PCA
Shakir Mohamed, Katherine Heller and Zoubin Ghahramani
In: NIPS 2008, Vancouver, Canada(2008).

Abstract

Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data. Ap- proaches such as exponential family PCA and non-negative matrix factorisation have successfully extended PCA to non-Gaussian data types, but these techniques fail to take advantage of Bayesian inference and can suffer from problems of over- fitting and poor generalisation. This paper presents a fully probabilistic approach to PCA, which is generalised to the exponential family, based on Hybrid Monte Carlo sampling. We describe the model which is based on a factorisation of the observed data matrix, and show performance of the model on both synthetic and real data.

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EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:6736
Deposited By:Katherine Heller
Deposited On:08 March 2010