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https://scholarbank.nus.edu.sg/handle/10635/71683
DC Field | Value | |
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dc.title | Robust PCA in high-dimension: A deterministic approach | |
dc.contributor.author | Feng, J. | |
dc.contributor.author | Xu, H. | |
dc.contributor.author | Yan, S. | |
dc.date.accessioned | 2014-06-19T03:26:34Z | |
dc.date.available | 2014-06-19T03:26:34Z | |
dc.date.issued | 2012 | |
dc.identifier.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. | |
dc.identifier.isbn | 9781450312851 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/71683 | |
dc.description.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). | |
dc.source | Scopus | |
dc.type | Conference Paper | |
dc.contributor.department | MECHANICAL ENGINEERING | |
dc.contributor.department | ELECTRICAL & COMPUTER ENGINEERING | |
dc.description.sourcetitle | Proceedings of the 29th International Conference on Machine Learning, ICML 2012 | |
dc.description.volume | 1 | |
dc.description.page | 249-256 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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