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The perplexing effects of noise and high feature dimensionality greatly complicate functional magnetic resonance imaging (fMRI) classification. In this paper, we present a novel formulation for constructing "Generalized Group Sparse Classifiers" (GSSC) to alleviate these problems. In particular, we propose an extension of group LASSO that permits associations between features within (predefined) groups to be modeled. Integrating this new penalty into classifier learning enables incorporation ofdoi:10.1109/cvpr.2011.5995651 dblp:conf/cvpr/NgA11 fatcat:ng2un33wmvckjeldnugjibicyu