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.
|Title of host publication||Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference|
|Number of pages||8|
|Publication status||Published - 2009|
|Event||22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada|
Duration: 8 Dec 2008 → 11 Dec 2008
|Other||22nd Annual Conference on Neural Information Processing Systems, NIPS 2008|
|Period||8/12/08 → 11/12/08|