High dimensional Principal Component Analysis with contaminated data

We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for contaminated data in the high dimensional regime, where the the number of observations is of the same magnitude as the number of variables of each observation, and the data set contains some (arbitrarily) corrupted observations. We propose a high-dimensional robust principal component analysis (HR-PCA) algorithm that is tractable, robust to contaminated points, and easily kernelizable. The resulting subspace has a bounded deviation from the desired one, and unlike ordinary PCA algorithms, achieves optimality in the limit case where the proportion of corrupted points goes to zero. In this extended abstract we provide the setup, our algorithm, and a statement of the main theorems, and defer all the details and proofs to the full paper.