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

    Fingerprint

    Pipelines
    Oils

    Bibliographical note

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

    Keywords

    • NCRG

    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). Cambridge, MA: MIT.
    Bishop, Christopher M. ; Svens'en, M. ; Williams, Christopher K. I. / EM optimization of latent-variable density models. Advances in Neural Information Processing Systems 8. editor / D. S. Touretzky ; M. C. Mozer ; M. E. Hasselmo. Vol. 8 Cambridge, MA : MIT, 1996. pp. 465-471
    @inbook{e63162bf76804f9ca2d3ff6395c2f851,
    title = "EM optimization of latent-variable density models",
    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.",
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    author = "Bishop, {Christopher M.} and M. Svens'en and Williams, {Christopher K. I.}",
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    year = "1996",
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    Bishop, CM, Svens'en, M & Williams, CKI 1996, EM optimization of latent-variable density models. in DS Touretzky, MC Mozer & ME Hasselmo (eds), Advances in Neural Information Processing Systems 8. vol. 8, MIT, Cambridge, MA, pp. 465-471, Advances in Neural Information Processing Systems 1996, Hong Kong, China, 12/11/96.

    EM optimization of latent-variable density models. / Bishop, Christopher M.; Svens'en, M.; Williams, Christopher K. I.

    Advances in Neural Information Processing Systems 8. ed. / D. S. Touretzky; M. C. Mozer; M. E. Hasselmo. Vol. 8 Cambridge, MA : MIT, 1996. p. 465-471.

    Research output: Chapter in Book/Report/Conference proceedingChapter

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    T1 - EM optimization of latent-variable density models

    AU - Bishop, Christopher M.

    AU - Svens'en, M.

    AU - Williams, Christopher K. I.

    N1 - Copyright of the Massachusetts Institute of Technology Press (MIT Press)

    PY - 1996/6

    Y1 - 1996/6

    N2 - 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.

    AB - 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.

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    SN - 0262201070

    VL - 8

    SP - 465

    EP - 471

    BT - Advances in Neural Information Processing Systems 8

    A2 - Touretzky, D. S.

    A2 - Mozer, M. C.

    A2 - Hasselmo, M. E.

    PB - MIT

    CY - Cambridge, MA

    ER -

    Bishop CM, Svens'en M, Williams CKI. EM optimization of latent-variable density models. In Touretzky DS, Mozer MC, Hasselmo ME, editors, Advances in Neural Information Processing Systems 8. Vol. 8. Cambridge, MA: MIT. 1996. p. 465-471