Poster
Competing RBM density models for classification of fMRI images
Tanya Schmah · Geoffrey E Hinton · Richard Zemel

Mon Dec 8th 08:45 PM -- 12:00 AM @ None #None

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- regularised. 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 use discriminative fitting of exactly the same set of models to demonstrate that the superior discrimination performance is caused by the generative fitting rather than the type of model. We used restricted Boltzmann machines as our generative models, but our results suggest that many other generative models should be tried for discriminating different conditions in neuroimaging data.

Author Information

Tanya Schmah (University of Ottawa)
Geoffrey E Hinton (Google & University of Toronto)

Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member at Carnegie-Mellon where he pioneered back-propagation, Boltzmann machines and distributed representations of words. In 1987 he became a fellow of the Canadian Institute for Advanced Research and moved to the University of Toronto. In 1998 he founded the Gatsby Computational Neuroscience Unit at University College London, returning to the University of Toronto in 2001. His group at the University of Toronto then used deep learning to change the way speech recognition and object recognition are done. He currently splits his time between the University of Toronto and Google. In 2010 he received the NSERC Herzberg Gold Medal, Canada's top award in Science and Engineering.

Richard Zemel (Vector Institute/University of Toronto)

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