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IPMI
2011
Springer

Generalized Sparse Regularization with Application to fMRI Brain Decoding

8 years 1 months ago
Generalized Sparse Regularization with Application to fMRI Brain Decoding
Many current medical image analysis problems involve learning thousands or even millions of model parameters from extremely few samples. Employing sparse models provides an effective means for handling the curse of dimensionality, but other propitious properties beyond sparsity are typically not modeled. In this paper, we propose a simple approach, generalized sparse regularization (GSR), for incorporating domain-specific knowledge into a wide range of sparse linear models, such as the LASSO and group LASSO regression models. We demonstrate the power of GSR by building anatomically-informed sparse classifiers that additionally model the intrinsic spatiotemporal characteristics of brain activity for fMRI classification. We validate on real data and show how prior-informed sparse classifiers outperform standard classifiers, such as SVM and a number of sparse linear classifiers, both in terms of prediction accuracy and result interpretability. Our results illustrate the addedvalue in faci...
Bernard Ng, Rafeef Abugharbieh
Added 30 Aug 2011
Updated 30 Aug 2011
Type Journal
Year 2011
Where IPMI
Authors Bernard Ng, Rafeef Abugharbieh
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