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|Title:||Robust PCA in high-dimension: A deterministic approach||Authors:||Feng, J.
|Issue Date:||2012||Citation:||Feng, J.,Xu, H.,Yan, S. (2012). Robust PCA in high-dimension: A deterministic approach. Proceedings of the 29th International Conference on Machine Learning, ICML 2012 1 : 249-256. ScholarBank@NUS Repository.||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. Copyright 2012 by the author(s)/owner(s).||Source Title:||Proceedings of the 29th International Conference on Machine Learning, ICML 2012||URI:||http://scholarbank.nus.edu.sg/handle/10635/71683||ISBN:||9781450312851|
|Appears in Collections:||Staff Publications|
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