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The group Lasso is an extension of the Lasso or l1-penalised least squares procedure. It forces simultaneous zeroing of groups of variables and has already been applied to sparse component analysis and ill-conditioned inverse problems. In this paper we address the unresolved problem of threshold or penalty parameter selection.
PCA has found use as an exploratory technique for fMRI analysis. However underlying it is an implicit model that while allowing temporal non-stationary covariance assumes the same covariance structure for all voxels. Here we relax this assumption for the first time by developing a version of PCA that allows the covariance structure to vary spatially. The new method is applied to real data and provides...
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