Fetching the paper…
Reading the bibliography…
This paper revisits the problem of decomposing a positive semidefinite matrix as a sum of a matrix with a given rank plus a sparse matrix.
Fast Algorithms for Robust PCA via Gradient Descent
X. Yi, D. Park, Y. Chen, and C. Caramanis · 1908
Earlier work this paper cites.
Multiplier and gradient methods
M. Hestenes · 1969
Earlier work this paper cites.
Numerical Optimization
J. Nocedal and S. Wright · 2000
Earlier work this paper cites.
Smooth minimization of non-smooth functions
Y. Nesterov · 2005
Earlier work this paper cites.
Latent variable graphical model selection via convex optimization
V. Chandrasekaran, P. Parrilo, and A. Willsky · 2010
Earlier work this paper cites.
The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices
Z. Lin, M. Chen, and Y. Ma · 2010
Earlier work this paper cites.
Decomposing background topics from keywords by principal component pursuit
K. Min, Z. Zhang, J. Wright, and Y. Ma · 2010
Earlier work this paper cites.
Robust principal component analysis?
E. Candès, X. Li, Y. Ma, and J. Wright · 2011
Earlier work this paper cites.
Incremental gradient on the grassmannian for online foreground and background separation in subsampled video
J. He, L. Balzano, and A. Szlam · 2012
Earlier work this paper cites.
A probabilistic approach to robust matrix factorization
N. Wang, T. Yao, J. Wang, and D. Yeung · 2012
Cited alongside, same era.
Alternating direction methods for latent variable gaussian graphical model selection
N. Aybat, L. Xue, and H. Zou · 2013
Cited alongside, same era.
Introductory lectures on convex optimization: A basic course
Y. Nesterov · 2013
Cited alongside, same era.
Fast principal component pursuit via alternating minimization
P. Rodriguez and B. Wohlberg · 2013
Cited alongside, same era.
Moving object detection by detecting contiguous outliers in the low-rank representation
X. Zhou, C. Yang, and W. Yu · 2013
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Robust Principal Component Analysis on graphs
N. Shahid, V. Kalofolias, X. Bresson, M. Bronstein, and P. Vandergheynst · 2015
Later among the works it cites.
Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing
T. Bouwmans, N. Aybat, and E. Zahzah · 2016
Later among the works it cites.
Accelerated gradient methods for nonconvex nonlinear and stochastic programming
S. Ghadimi and G. Lan · 2016
Later among the works it cites.
Bounds for the distance to the nearest correlation matrix
N. Higham and N. Strabic · 2016
Later among the works it cites.
Incremental principal component pursuit for video background modeling
P. Rodriguez and B. Wohlberg · 2016
Later among the works it cites.
Identifying broad and narrow financial risk factors with convex optimization
A. Shkolnik, L. Goldberg, and J. Bohn · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Lrslibrary: Low-rank and sparse tools for background modeling and subtraction in videos
T. Bouwmans, A. Sobral, and E. Zahzah · 2015
Cited alongside, same era.
Robust PCA Via Nonconvex Rank Approximation
Z. Kang, C. Peng, Q. Cheng · 2015
Cited alongside, same era.
RPCA-based tumor classification using gene expression data
J. Liu, Y. Xu, C. Zheng, H. Kong, and Z. Lai · 2015
Cited alongside, same era.
Later among the works it cites.
Decomposition into low-rank plus additive matrices for background/foreground separation: A review for a comparative evaluation with a large-scale dataset
T. Bouwmans, A. Sobral, S. Javed, S. Jung, and E. Zahzah · 2017
Later among the works it cites.
Automatic differentiation in Pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Later among the works it cites.
Easing non-convex optimization with neural networks
D. Lopez-Paz and L. Sagun · 2018
Later among the works it cites.