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The perplexing effects of noise and high feature dimensionality greatly complicate functional magnetic
 

Summary: 1065
Abstract
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 of
additional prior information beyond group structure. In
the context of fMRI, GGSC provides a flexible means for
modeling how the brain is functionally organized into
specialized modules (i.e. groups of voxels) with spatially
proximal voxels often displaying similar level of brain
activity (i.e. feature associations). Applying GSSC to real
fMRI data improved predictive performance over standard
classifiers, while providing more neurologically
interpretable classifier weight patterns. Our results thus

  

Source: Abugharbieh, Rafeef - Department of Electrical and Computer Engineering, University of British Columbia

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences