Fri Nov 02 2012

Machine Learning##### Minimax sparse principal subspace estimation in high dimensions

###### We study sparse principal components analysis in high dimensions. We prove nonasymptotic lower and upper bounds on the minimax estimation error. The bounds are optimal for row and column sparse subspaces. They apply to general classes of covariance matrices.

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