Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/71683
Title: Robust PCA in high-dimension: A deterministic approach
Authors: Feng, J.
Xu, H. 
Yan, S. 
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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