Publications [#258369] of Katherine Heller
Papers Published
- Mohamed, S; Heller, K; Ghahramani, Z. "Bayesian exponential family PCA." Advances in Neural Information Processing Systems 21 Proceedings of the 2008 Conference (December, 2009): 1089-1096.
(last updated on 2021/07/07)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.