David J. Eis

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A new approach to regression regulariza-tion called the Pairwise Elastic Net is proposed. Like the Elastic Net, it simultaneously performs automatic variable selection and continuous shrinkage. In addition, the Pairwise Elastic Net encourages the grouping of strongly correlated predictors based on a pairwise similarity measure. We give examples of how the(More)
We investigate connections between the generalized lasso and the standard lasso problem. We show by an efficient direct construction, that the generalized lasso problem is reducible to a subspace constrained lasso. We then derive the dual of the subspace constrained lasso. This dual problem can be projected to the dual of a standard lasso problem with a(More)
We propose a novel collaborative denoising scheme for multi-subject fMRI data. The scheme assumes that subjects experience a common, synchronous stimulus and uses the across-subject shared response structure to jointly denoise each subject's fMRI response along the spatial or voxel domain. Denoising is accomplished by learning subject-specfic orthonormal(More)
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