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2011
IEEE

Generalized Group Sparse Classifiers with Application in fMRI Brain Decoding

8 years 2 months ago
Generalized Group Sparse Classifiers with Application in fMRI Brain Decoding
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 res...
Bernard Ng, Rafeef Abugharbieh
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where CVPR
Authors Bernard Ng, Rafeef Abugharbieh
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