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PCA is one of the most widely used dimension reduction techniques.
“Robust Procedures in Multivariate Analysis I: Robust Covariance Estimations,”
N. Campell, · 1980
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“Robust Statistics,”
P. Huber, · 1981
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“Robust principal component analysis by self-organizing rules based on statistical physics approach,”
L. Xu and A. Yuille, · 1995
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“Projection approximation subspace tracking,”
B. Yang, · 1995
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“A Fast Algorithm for Robust Principal Components Based on Projection Pursuit,”
C. Croux and A. Ruiz-Gazen, · 1996
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“A framework for robust subspace learning,”
F. De La Torre and M. J. Black, · 2003
Earlier work this paper cites.
“An integrated algorithm of incremental and robust pca,”
Y. Li, L. Xu, J. Morphett, and R. Jacobs, · 2003
Earlier work this paper cites.
“Weighted and robust incremental method for subspace learning,”
D. Skocaj and A. Leonardis, · 2003
Earlier work this paper cites.
“ROBPCA: A New Approach to Robust Principal Component Analysis,”
M. Hubert, P. Rousseeuw, and K. Vanden Branden, · 2005
Earlier work this paper cites.
“The restricted isometry property and its implications for compressed sensing,”
E. Candes, · 2008
Earlier work this paper cites.
“Dense error correction via l1-minimization,”
J. Wright and Y. Ma, · 2010
Earlier work this paper cites.
“Real-time robust principal components’ pursuit,”
C. Qiu and N. Vaswani, · 2010
Earlier work this paper cites.
“Online Identification and Tracking of Subspaces from Highly Incomplete Information,”
L. Balzano, B. Recht, and R. Nowak, · 2010
Earlier work this paper cites.
“Modified-CS: Modifying compressive sensing for problems with partially known support,”
N. Vaswani and W. Lu, · 2010
Earlier work this paper cites.
“Robust principal component analysis?,”
E. J. Candès, X. Li, Y. Ma, and J. Wright, · 2011
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“Rank-sparsity incoherence for matrix decomposition,”
V. Chandrasekaran, S. Sanghavi, P. A. Parrilo, and A. S. Willsky, · 2011
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“Robust matrix decomposition with sparse corruptions,”
D. Hsu, S. M. Kakade, and T. Zhang, · 2011
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“Robust matrix completion with corrupted columns,”
Y. Chen, H. Xu, C. Caramanis, and S. Sanghavi, · 2011
Earlier work this paper cites.
“Recursive sparse recovery in large but correlated noise,”
C. Qiu and N. Vaswani, · 2011
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“Sparcs: Recovering low-rank and sparse matrices from compressive measurements,”
A. E. Waters, A. C. Sankaranarayanan, and R. G. Baraniuk, · 2011
Earlier work this paper cites.
“Two proposals for robust pca using semidefinite programming,”
M. McCoy and J. Tropp, · 2011
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“Godec: Randomized low-rank and sparse matrix decomposition in noisy case,”
T. Zhou and D. Tao, · 2011
Earlier work this paper cites.
“Support predicted modified-cs for recursive robust principal components’ pursuit,”
C. Qiu and N. Vaswani, · 2011
Earlier work this paper cites.
“Robust pca as bilinear decomposition with outlier-sparsity regularization,”
G. Mateos and G. Giannakis, · 2012
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.
“Robust pca via outlier pursuit,”
H. Xu, C. Caramanis, and S. Sanghavi, · 2012
Earlier work this paper cites.
“A Probabilistic approach to Robust Matrix Factorization,”
N. Wang, T. Yao, J. Wang, and D. Y. Yeung, · 2012
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“Recovery of low-rank plus compressed sparse matrices with application to unveiling traffic anomalies,”
M. Mardani, G. Mateos, and G. B. Giannakis, · 2013
Cited alongside, same era.
“Low-rank and sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components,”
R. Otazo, E. Candès, and D. K. Sodickson, · 2013
Cited alongside, same era.
“Dynamic anomalography: Tracking network anomalies via sparsity and low rank,”
M. Mardani, G. Mateos, and G. B. Giannakis, · 2013
Cited alongside, same era.
“Outlier-robust pca: the high-dimensional case,”
H. Xu, C. Caramanis, and S. Mannor, · 2013
Cited alongside, same era.
“Online pca for contaminated data,”
J. Feng, H. Xu, S. Mannor, and S. Yan, · 2013
Cited alongside, same era.
“Petrels: Parallel subspace estimation and tracking by recursive least squares from partial observations,”
“Background subtraction via generalized fused lasso foreground modeling,”
B. Xin, Y. Tian, Y. Wang, and W. Gao, · 2015
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“Robust background subtraction to global illumination changes via multiple features-based online robust principal components analysis with markov random field,”
S. Javed, S. H. Oh, T. Bouwmans, and S. K. Jung, · 2015
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“Subsense: A universal change detection method with local adaptive sensitivity,”
Pierre-Luc St-Charles, Guillaume-Alexandre Bilodeau, and Robert Bergevin, · 2015
Later among the works it cites.
“Online (and Offline) Robust PCA: Novel Algorithms and Performance Guarantees,”
J. Zhan, B. Lois, H. Guo, and N. Vaswani, · 2016
Later among the works it cites.
“Fast algorithms for robust pca via gradient descent,”
X. Yi, D. Park, Y. Chen, and C. Caramanis, · 2016
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Y. Chi, Y. C. Eldar, and R. Calderbank, · 2013
Cited alongside, same era.
“Online robust pca via stochastic optimization,”
J. Feng, H. Xu, and S. Yan, · 2013
Cited alongside, same era.
F. Seidel, C. Hage, and M. Kleinsteuber, · 2013
Cited alongside, same era.
“Gosus: Grassmannian online subspace updates with structured-sparsity,”
J. Xu, V. Ithapu, L. Mukherjee, J. Rehg, and V. Singh, · 2013
Cited alongside, same era.
“Simultaneous video stabilization and moving object detection in turbulence,”
O. Oreifej, X. Li, and M. Shah, · 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.
“Performance guarantees for undersampled recursive sparse recovery in large but structured noise,”
B. Lois, N. Vaswani, and C. Qiu, · 2013
Cited alongside, same era.
Y. Cherapanamjeri, K. Gupta, and P. Jain, · 2016
Later among the works it cites.
“Video denoising via online sparse and low-rank matrix decomposition,”
H. Guo and N. Vaswani, · 2016
Later among the works it cites.
“LRS Library: Low-Rank and Sparse tools for Background Modeling and Subtraction in videos,”
A. Sobral, T. Bouwmans, and E. H. Zahzah, · 2016
Later among the works it cites.
“Correlated-pca: Principal components’ analysis when data and noise are correlated,”
N. Vaswani and H. Guo, · 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.
“Total Variation Regularized RPCA for Irregularly Moving Object Detection Under Dynamic Background,”
X. Cao, L. Yang, and X. Guo, · 2016
Later among the works it cites.
“Spatiotemporal low-rank modeling for complex scene background initialization,”
S. Javed, A. Mahmood, T. Bouwmans, and S. K. Jung, · 2016
Later among the works it cites.
“COROLA: A sequential solution to moving object detection using low-rank approximation,”
S. Moein and Z. Hong, · 2016
Later among the works it cites.
“Universal background subtraction using word consensus models,”
Charles S and Robert Bergevin, · 2016
Later among the works it cites.
“Provable non-convex phase retrieval with outliers: Median truncated wirtinger flow,”
H. Zhang, Y. Chi, and Y. Liang, · 2016
Later among the works it cites.
“Recursive tensor subspace tracking for dynamic brain network analysis,”
A. Ozdemir, E. M. Bernat, and S. Aviyente, · 2017
Closest in time.
“Nearly optimal robust subspace tracking,”
P. Narayanamurthy and N. Vaswani, · 2017
Closest in time.
“Finite sample guarantees for pca in non-isotropic and data-dependent noise,”
N. Vaswani and P. Narayanamurthy, · 2017
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“Background-Foreground Modeling Based on Spatiotemporal Sparse Subspace Clustering,”
S. Javed, A. Mahmood, T. Bouwmans, and S. K. Jung, · 2017
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“Robust online matrix factorization for dynamic background subtraction,”
H. Yong, D. Meng, W. Zuo, and L. Zhang, · 2017
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“Low rank phase retrieval,”
N. Vaswani, S. Nayer, and Y. C. Eldar, · 2017
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“Reinforced robust principal component pursuit,”
P. P. Brahma, Y. She, S. Li, J. Li, and D. Wu, · 2017
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“Robust principal component analysis (pca) and matrix completion: A review,”
N. Vaswani and P. Narayanamurthy, · 2018
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“An overview of robust subspace recovery,”
G. Lerman et al., · 2018
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“A Fast and Memory-Efficient Algorithm for Robust PCA (MERoP),”
P. Narayanamurthy and N. Vaswani, · 2018
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“Provable dynamic robust pca or robust subspace tracking,”
P. Narayanamurthy and N. Vaswani, · 2018
Closest in time.