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Recent methods for learning a linear subspace from data corrupted by outliers are based on convex $\ell_1$ and nuclear norm optimization and require the dimension of the subspace and the number of outliers to be sufficiently small.
On convergence rates of subgradient optimization methods
Jean-Louis Goffin · 1977
Earlier work this paper cites.
RANSAC random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
M. A. Fischler and R. C. Bolles · 1981
Earlier work this paper cites.
Principal Component Analysis
I. Jolliffe · 1986
Earlier work this paper cites.
On orthogonal linear ℓ 1 \ell_{1} approximation
H. Späth and G.A. Watson · 1987
Earlier work this paper cites.
A distribution-free m-estimator of multivariate scatter
D. E. Tyler · 1987
Earlier work this paper cites.
On the method of bounded differences
Colin McDiarmid · 1989
Earlier work this paper cites.
Asymptotic statistics , volume 3
Aad W Van der Vaart · 1998
Earlier work this paper cites.
Subgradient methods
Stephen Boyd, Lin Xiao, and Almir Mutapcic · 2003
Earlier work this paper cites.
Multiple View Geometry in Computer Vision
R. Hartley and A. Zisserman · 2004
Earlier work this paper cites.
Numerical Optimization, second edition
Jorge Nocedal and Stephen J. Wright · 2006
Earlier work this paper cites.
An introduction to compressive sampling
E. Candès and M. Wakin · 2008
Earlier work this paper cites.
Aspects of multivariate statistical theory , volume 197
Robb J Muirhead · 2009
Earlier work this paper cites.
Online identification and tracking of subspaces from highly incomplete information
L. Balzano, R. Nowak, and B. Recht · 2010
Earlier work this paper cites.
Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
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Huan Xu, Constantine Caramanis, and Sujay Sanghavi · 2010
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Symmetrization and rademacher averages
S. Kakade · 2011
Cited alongside, same era.
Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
Cited alongside, same era.
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Michel Ledoux and Michel Talagrand · 2013
Cited alongside, same era.
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Mostafa Rahmani and George Atia · 2016
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Robust subspace recovery by tyler’s m-estimator
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Later among the works it cites.
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Gurobi optimizer reference manual, 2015
Inc. Gurobi Optimization · 2015
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