EM optimization of latent-variable density models


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.

Divisions: Aston University (General)
Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT Press)
Event Title: Advances in Neural Information Processing Systems 1996
Event Type: Other
Event Dates: 1996-11-12 - 1996-11-14
Uncontrolled Keywords: NCRG
ISBN: 0262201070
Last Modified: 30 Apr 2024 07:35
Date Deposited: 15 Jul 2009 09:46
Full Text Link:
Related URLs: http://mitpress ... type=2&tid=8421 (Publisher URL)
PURE Output Type: Chapter
Published Date: 1996-06
Authors: Bishop, Christopher M.
Svens'en, M.
Williams, Christopher K. I.



Version: Published Version

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