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Generalized Sparse Classifiers for Decoding Cognitive States in fMRI
 

Summary: Generalized Sparse Classifiers for Decoding
Cognitive States in fMRI
Bernard Ng1
, Arash Vahdat2
, Ghassan Hamarneh3
, Rafeef Abugharbieh1
1
Biomedical Signal and Image Computing Lab, The University of British Columbia
2
Vision and Media Lab, Simon Fraser University
3
Medical Image Analysis Lab, Simon Fraser University
bernardn@ece.ubc.ca
Abstract. The high dimensionality of functional magnetic resonance imaging
(fMRI) data presents major challenges to fMRI pattern classification. Directly
applying standard classifiers often results in overfitting, which limits the
generalizability of the results. In this paper, we propose a new group of
classifiers, "Generalized Sparse Classifiers" (GSC), to alleviate this overfitting
problem. GSC draws upon the recognition that numerous standard classifiers
can be reformulated under a regression framework, which enables state-of-the-

  

Source: Abugharbieh, Rafeef - Department of Electrical and Computer Engineering, University of British Columbia
Hamarneh, Ghassan - School of Computing Science, Simon Fraser University

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences