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Generalized Sparse Regularization with Application to fMRI Brain Decoding

Summary: Generalized Sparse Regularization with Application to
fMRI Brain Decoding
Bernard Ng and Rafeef Abugharbieh
Biomedical Signal and Image Computing Lab, UBC, Canada
Abstract. 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 added-
value in facilitating flexible integration of prior knowledge beyond sparsity in
large-scale model learning problems.


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


Collections: Biology and Medicine; Computer Technologies and Information Sciences