Edinburgh Research Explorer

EM optimization of latent-variable density models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 8 (NIPS 1995)
PublisherMIT Press
Pages465-471
Number of pages7
Publication statusPublished - 1996

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.

Download statistics

No data available

ID: 22152719