The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Modern classification techniques perform well when the number of training examples exceed the number of features. If, however, the number of features greatly exceed the number of training examples, then these same techniques can fail. To address this problem, we present a hierarchical Bayesian framework that shares information between features by modeling similarities between their parameters. We believe this approach is applicable to many sparse, high dimensional problems and especially relevant to those with both spatial and temporal components. One such problem is fMRI time series, and we present a case study that shows how we can successfully classify in this domain with 80,000 original features and only 2 training examples per class.

Keywords

classification, fmri, hierarchical bayes

Notes

Number of pages: 12

Text Reference

Mark Palatucci and Tom Mitchell, "Classification in Very High Dimensional Problems with Handfuls of Examples," Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), September, 2007.

BibTeX Reference

@inproceedings{Palatucci_2007_5786,
   author = "Mark Palatucci and Tom Mitchell",
   title = "Classification in Very High Dimensional Problems with Handfuls of Examples",
   booktitle = "Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD)",
   publisher = "Springer-Verlag",
   month = "September",
   year = "2007",
}

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