Computer Science Department
School of Computer Science, Carnegie Mellon University
Beyond Brain Blobs:
The thesis put forth in this dissertation is that machine learning classifiers can be used as instruments for decoding variables of interest from functional magnetic resonance imaging (ƒMRI) data. There are two main goals in decoding:
Chapter 2 considers the issues that arise when using traditional linear classifiers and several different voxel selection techniques to strive towards these goals. It examines questions such as whether there is redundant, as well as different kinds of, information in ƒMRI data and how those facts complicate the task of determining whether voxel subsets encode the desired information or whether certain choices of selection method or classifier are universally preferrable. All the results presented were obtained through a comparative study of five ƒMRI datasets.
Chapter 3 and Chapter 4 introduce the Support Vector Decomposition Machine, a new algorithm that attempts to sidestep the use of voxel selection by learning a low dimensional representation of the data that is also suitable for decoding variables of interest and has the potential to allow incorporation of domain information directly, rather than through the proxy of voxel selection criteria.