Katerina Gkirtzou

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k-support norm regularization to fMRI analysis.

An exploration of various sparse regularization techniques for analyzing fMRI data, such as the L1 norm (often called LASSO in the context of a squared loss function), Elastic Net, and k-support norm. Employing sparsity regularization allows us to handle the curse of dimensionality, a problem commonly found in fMRI analysis. In this work we consider sparse regularization in both the regression and classification settings. We perform experiments on fMRI scans from cocaine-addicted as well as healthy control subjects.

Publications

• Predictive sparse modeling of fMRI data for improved classication, regression, and visualization using the k-support norm.
  Belilovsky E., Gkirtzou K. Misyrlis M., Konova A.B., Honorio J., Alia-Klein N., Goldstein R., Samaras D., and Blaschko M.
  Computerized Medical Imaging and Graphics, 2015
  [pdf] [bibtex] [doi] [code]
• fMRI Analysis of Cocaine Addiction Using k-support Sparsity.
  Gkirtzou Katerina, Honorio Jean, Samaras Dimitris, Goldstein Rita and Blaschko B. Matthew
  International Symposium on Biomedical Imaging (ISBI), 2013.
  [pdf] [bibtex] [doi]
• Sparsity regularization and graph-based representation in medical imaging
  Gkirtzou Katerina
  PhD Thesis, Ecole Centrale de Paris, 2013.
  [pdf] [bibtex] [presentation]

Code