Shakir Mohamed



Bayesian Exponential Family PCA

S. Mohamed, K. Heller and Z. Ghahramani. Bayesian Exponential Family PCA. In Proceedings of the 21st Conference on Advances in Neural Information Processing Systems (NIPS 21). December 2008. »PDF »BibTeX


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.