Robust PCA in high-dimension: A deterministic approach

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

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Main Authors:	Feng, J., Xu, H., Yan, S.
Other Authors:	MECHANICAL ENGINEERING
Format:	Conference or Workshop Item
Published:	2014
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/71683
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-71683
record_format	dspace
spelling	sg-nus-scholar.10635-716832015-02-04T20:06:04Z Robust PCA in high-dimension: A deterministic approach Feng, J. Xu, H. Yan, S. MECHANICAL ENGINEERING ELECTRICAL & COMPUTER ENGINEERING Proceedings of the 29th International Conference on Machine Learning, ICML 2012 1 249-256 2014-06-19T03:26:34Z 2014-06-19T03:26:34Z 2012 Conference Paper 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. 9781450312851 http://scholarbank.nus.edu.sg/handle/10635/71683 NOT_IN_WOS Scopus
institution	National University of Singapore
building	NUS Library
country	Singapore
collection	ScholarBank@NUS
description	Proceedings of the 29th International Conference on Machine Learning, ICML 2012
author2	MECHANICAL ENGINEERING
author_facet	MECHANICAL ENGINEERING Feng, J. Xu, H. Yan, S.
format	Conference or Workshop Item
author	Feng, J. Xu, H. Yan, S.
spellingShingle	Feng, J. Xu, H. Yan, S. Robust PCA in high-dimension: A deterministic approach
author_sort	Feng, J.
title	Robust PCA in high-dimension: A deterministic approach
title_short	Robust PCA in high-dimension: A deterministic approach
title_full	Robust PCA in high-dimension: A deterministic approach
title_fullStr	Robust PCA in high-dimension: A deterministic approach
title_full_unstemmed	Robust PCA in high-dimension: A deterministic approach
title_sort	robust pca in high-dimension: a deterministic approach
publishDate	2014
url	http://scholarbank.nus.edu.sg/handle/10635/71683
_version_	1681087428461330432

Robust PCA in high-dimension: A deterministic approach

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