Bayesian exponential family PCA

Published

Journal Article

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.

Duke Authors

Cited Authors

  • Mohamed, S; Heller, K; Ghahramani, Z

Published Date

  • December 1, 2009

Published In

  • Advances in Neural Information Processing Systems 21 Proceedings of the 2008 Conference

Start / End Page

  • 1089 - 1096

Citation Source

  • Scopus