We consider principal component analysis for contaminated data-set in the high dimensional regime, where the number of observations is comparable or more than the dimensionality of each observation. We propose a *deterministic* high-dimensional robust PCA algorithm which inherits all theoretical properties of its *randomized* counterpart, i.e., it is tractable, robust to contaminated points, easily kernelizable, asymptotic consistent and achieves maximal robustness - a breakdown point of 50%. More importantly, the proposed method exhibits significantly better computational efficiency, which makes it suitable for large-scale real applications.