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Recent results on sparse principle component analysis

Principal component analysis (PCA) is one of the most commonly used statistical procedures for dimension reduction. This paper presents some recent results on the minimax estimation of principal subspaces in high dimensions. Under mild technical conditions, we characterize the minimax risk for estimating the principal subspace under the quadratic loss within absolute constant factors.