EM optimization of latent-variable density models

Christopher M. Bishop, M. Svens'en, Christopher K. I. Williams

    Research output: Chapter in Book/Report/Conference proceedingChapter

    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.
    Original languageEnglish
    Title of host publicationAdvances in Neural Information Processing Systems 8
    EditorsD. S. Touretzky, M. C. Mozer, M. E. Hasselmo
    Place of PublicationCambridge, MA
    PublisherMIT
    Pages465-471
    Number of pages7
    Volume8
    ISBN (Print)0262201070
    Publication statusPublished - Jun 1996
    EventAdvances in Neural Information Processing Systems 1996 - Hong Kong, China
    Duration: 12 Nov 199614 Nov 1996

    Conference

    ConferenceAdvances in Neural Information Processing Systems 1996
    CountryChina
    CityHong Kong
    Period12/11/9614/11/96

    Bibliographical note

    Copyright of the Massachusetts Institute of Technology Press (MIT Press)

    Keywords

    • NCRG

    Fingerprint Dive into the research topics of 'EM optimization of latent-variable density models'. Together they form a unique fingerprint.

  • Cite this

    Bishop, C. M., Svens'en, M., & Williams, C. K. I. (1996). EM optimization of latent-variable density models. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8 (Vol. 8, pp. 465-471). MIT.