PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian Exponential Family PCA
Shakir Mohamed, Katherine Heller and Zoubin Ghahramani
In: Advances in Neural Information Processing (NIPS), 7 - 13 Dec 2008, Vancouver, Canada.

Abstract

Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data. Approaches 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 overfitting 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:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4380
Deposited By:Shakir Mohamed
Deposited On:13 March 2009