Generative versus discriminative training of RBMs for classification of fMRI images

Tanya Schmah*, Geoffrey E. Hinton, Richard S. Zemel, Steven L. Small, Stephen Strother

*Corresponding author for this work

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

    54 Citations (Scopus)

    Abstract

    Neuroimaging datasets often have a very large number of voxels and a very small number of training cases, which means that overfitting of models for this data can become a very serious problem. Working with a set of fMRI images from a study on stroke recovery, we consider a classification task for which logistic regression performs poorly, even when L1- or L2- regularized. We show that much better discrimination can be achieved by fitting a generative model to each separate condition and then seeing which model is most likely to have generated the data. We compare discriminative training of exactly the same set of models, and we also consider convex blends of generative and discriminative training.

    Original languageEnglish
    Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
    Pages1409-1416
    Number of pages8
    Publication statusPublished - 2009
    Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
    Duration: 8 Dec 200811 Dec 2008

    Other

    Other22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
    CountryCanada
    CityVancouver, BC
    Period8/12/0811/12/08

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