Mitchell T., Hutchinson R., Niculescu S., Pereira F., Wang X., Just M., Newman S.
Machine Learning Journal, Vol. 57, Issue 1-2, pp. 145-175, 2004
[
abstract]
Over the past decade, functional Magnetic Resonance Imaging (fMRI) has
emerged as a powerful new instrument to collect vast quantities of data about
activity in the human brain. A typical fMRI experiment can produce a
three-dimensional image related to the human subject's brain activity every
half second, at a spatial resolution of a few millimeters. As in other
modern empirical sciences, this new instrumentation has led to a flood of new
data, and a corresponding need for new data analysis methods. We describe
recent research applying machine learning methods to the problem of
classifying the cognitive state of a human subject based on fRMI data
observed over a single time interval. In particular, we present case studies
in which we have successfully trained classifiers to distinguish cognitive
states such as (1) whether the human subject is looking at a picture or a
sentence, (2) whether the subject is reading an ambiguous or non-ambiguous
sentence, (3) whether the word the subject is viewing is a noun or a verb,
and (4) whether the noun the subject is viewing is a word describing food,
people, buildings, etc. This learning problem provides an interesting case
study of classifier learning from extremely high dimensional ($10^5$
features), extremely sparse (tens of training examples), noisy data. This
paper summarizes the results obtained in these four case studies, as well as
lessons learned about how to successfully apply machine learning methods to
train classifiers in such settings.
