Generative versus discriminative training of RBMs for classification of fMRI images

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

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

54 Scopus citations

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 (US)
Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Pages1409-1416
Number of pages8
StatePublished - Dec 1 2009
Externally publishedYes
Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
Duration: Dec 8 2008Dec 11 2008

Publication series

NameAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Conference

Conference22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
CountryCanada
CityVancouver, BC
Period12/8/0812/11/08

ASJC Scopus subject areas

  • Information Systems

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    Schmah, T., Hinton, G. E., Zemel, R. S., Small, S. L., & Strother, S. (2009). Generative versus discriminative training of RBMs for classification of fMRI images. In Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference (pp. 1409-1416). (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).