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arXiv · 1206.4628

Robust PCA in High-dimension: A Deterministic Approach

Jiashi Feng·Huan Xu·Shuicheng Yan
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Abstract

We consider principal component analysis for contaminated data-set in the high dimensional regime, where the dimensionality of each observation is comparable or even more than the number of observations. 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.

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Jiashi Feng, Huan Xu, Shuicheng Yan. 2012-06-18. Robust PCA in High-dimension: A Deterministic Approach. https://arxiv.org/abs/1206.4628

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