EM Optimization of Latent-Variable Density Models
Abstract
There is currently considerable interest in developing general non(cid:173) 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. Unfortu(cid:173) nately, 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.
Cite
Text
Bishop et al. "EM Optimization of Latent-Variable Density Models." Neural Information Processing Systems, 1995.Markdown
[Bishop et al. "EM Optimization of Latent-Variable Density Models." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/bishop1995neurips-em/)BibTeX
@inproceedings{bishop1995neurips-em,
title = {{EM Optimization of Latent-Variable Density Models}},
author = {Bishop, Christopher M. and Svensén, Markus and Williams, Christopher K. I.},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {465-471},
url = {https://mlanthology.org/neurips/1995/bishop1995neurips-em/}
}