| Document Date: 2015-04-10 08:40:31
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City Palaiseau / Mount Sinai / Cambridge / / Company fMRI / / Country France / United States / Greece / / / Facility Stony Brook University / / / IndustryTerm sparse solution / magnetic resonance imaging / functional magnetic resonance imaging / / NaturalFeature Sinai / / Organization Icahn School of Medicine / MIT / Ecole Polytechnique / Department of Psychology / Stony Brook University / / Person Katerina Gkirtzoua / Dimitris Samarasd / Rita Z. Goldsteinf / Nelly Alia-Kleinf / Michail Misyrlis / Jean Honoriog / Anna B. Konovae / Nelly Alia-Klein / Michail Misyrlisd / Katerina Gkirtzou / Eugene Belilovsky / Dimitris Samaras / Matthew Blaschko / Matthew B. Blaschkob / Anna Konova / Rita Goldstein / Jean Honorio / / / Position Corresponding author / / ProvinceOrState Rhode Island / New York / / Technology neuroscience / magnetic resonance imaging / machine learning / Medical Imaging / /
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Predictive sparse modeling of fMRI data for improved classification, regression, and visualization using the k-support norm Eugene Belilovsky, Katerina Gkirtzou, Michail Misyrlis, Anna Konova, Jean Honorio, Nelly Alia-Kl