@inproceedings{bishop1996em,
author = {Bishop, Christopher and Svensén, Markus and Williams, C. K. I.},
title = {EM optimization of latent variable density models},
booktitle = {Advances in Neural Information Processing Systems},
year = {1996},
month = {January},
abstract = {There is currently considerable interest in developing general non-linear density models based on latent, or hidden, variables. Such models have the ability to discover the presence of a relatively small number of underlying `causes' which, acting in combination, give rise to the apparent complexity of the observed data set. Unfortunately, to train such models generally requires large computational effort. In this paper we introduce a novel latent variable algorithm which retains the general non-linear capabilities of previous models but which uses a training procedure based on the EM algorithm. We demonstrate the performance of the model on a toy problem and on data from flow diagnostics for a multi-phase oil pipeline.},
publisher = {MIT Press},
url = {http://approjects.co.za/?big=en-us/research/publication/em-optimization-of-latent-variable-density-models/},
pages = {465-471},
volume = {8},
edition = {Advances in Neural Information Processing Systems},
}