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Robust principal component analysis (RPCA) is a critical tool in modern machine learning, which detects outliers in the task of low-rank matrix reconstruction.
Robust face recognition via sparse representation
John Wright, Allen Y Yang, Arvind Ganesh, S Shankar Sastry, and Yi Ma · 2008
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Fault detection and isolation with robust principal component analysis
Yvon Tharrault, Gilles Mourot, José Ragot, and Didier Maquin · 2008
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Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization
John Wright, Arvind Ganesh, Shankar Rao, Yigang Peng, and Yi Ma · 2009
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The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices
Zhouchen Lin, Minming Chen, and Yi Ma · 2010
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Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
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A singular value thresholding algorithm for matrix completion
Jian-Feng Cai, Emmanuel J Candès, and Zuowei Shen · 2010
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Robust principal component analysis?
Emmanuel J Candès, Xiaodong Li, Yi Ma, and John Wright · 2011
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Robust matrix decomposition with sparse corruptions
Daniel Hsu, Sham M Kakade, and Tong Zhang · 2011
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Rank-sparsity incoherence for matrix decomposition
Venkat Chandrasekaran, Sujay Sanghavi, Pablo A Parrilo, and Alan S Willsky · 2011
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Singing-voice separation from monaural recordings using robust principal component analysis
Po-Sen Huang, Scott Deeann Chen, Paris Smaragdis, and Mark Hasegawa-Johnson · 2012
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RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images
Yigang Peng, Arvind Ganesh, John Wright, Wenli Xu, and Yi Ma · 2012
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Clustering sparse graphs
Yudong Chen, Sujay Sanghavi, and Huan Xu · 2012
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Low-rank matrix recovery from errors and erasures
Yudong Chen, Ali Jalali, Sujay Sanghavi, and Constantine Caramanis · 2013
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Non-convex robust PCA
Praneeth Netrapalli, UN Niranjan, Sujay Sanghavi, Animashree Anandkumar, and Prateek Jain · 2014
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RPCA-based tumor classification using gene expression data
Jin-Xing Liu, Yong Xu, Chun-Hou Zheng, Heng Kong, and Zhi-Hui Lai · 2014
Earlier work this paper cites.
Scalable robust principal component analysis using Grassmann averages
Søren Hauberg, Aasa Feragen, Raffi Enficiaud, and Michael J Black · 2015
Earlier work this paper cites.
Euclidean distance matrices: essential theory, algorithms, and applications
Ivan Dokmanic, Reza Parhizkar, Juri Ranieri, and Martin Vetterli · 2015
Cited alongside, same era.
Low-rank and sparse structure pursuit via alternating minimization
Quanquan Gu, Zhaoran Wang, and Han Liu · 2016
Cited alongside, same era.
Fast algorithms for robust PCA via gradient descent
Xinyang Yi, Dohyung Park, Yudong Chen, and Constantine Caramanis · 2016
Cited alongside, same era.
Primary object segmentation in videos via alternate convex optimization of foreground and background distributions
Won-Dong Jang, Chulwoo Lee, and Chang-Su Kim · 2016
Cited alongside, same era.
Maximal sparsity with deep networks?
Bo Xin, Yizhou Wang, Wen Gao, David Wipf, and Baoyuan Wang · 2016
Cited alongside, same era.
Deep ADMM-Net for compressive sensing MRI
Yan Yang, Jian Sun, Huibin Li, and Zongben Xu · 2016
Deep unfolded robust PCA with application to clutter suppression in ultrasound
Oren Solomon, Regev Cohen, Yi Zhang, Yi Yang, Qiong He, Jianwen Luo, Ruud JG van Sloun, and Yonina C Eldar · 2019
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ALISTA: Analytic weights are as good as learned weights in LISTA
Jialin Liu, Xiaohan Chen, Zhangyang Wang, and Wotao Yin · 2019
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Understanding and correcting pathologies in the training of learned optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, and Jascha Sohl-Dickstein · 2019
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Rapid robust principal component analysis: CUR accelerated inexact low rank estimation
HanQin Cai, Keaton Hamm, Longxiu Huang, Jiaqi Li, and Tao Wang · 2020
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Intrinsic Grassmann averages for online linear, robust and nonlinear subspace learning
Rudrasis Chakraborty, Liu Yang, Soren Hauberg, and Baba Vemuri · 2020
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Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
Cited alongside, same era.
Nearly optimal robust matrix completion
Yeshwanth Cherapanamjeri, Kartik Gupta, and Prateek Jain · 2017
Cited alongside, same era.
Learned D-AMP: Principled neural network based compressive image recovery
Christopher A Metzler, Ali Mousavi, and Richard G Baraniuk · 2017
Cited alongside, same era.
Learned optimizers that scale and generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando Freitas, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing
Jian Zhang and Bernard Ghanem · 2018
Cited alongside, same era.
Learned primal-dual reconstruction
Jonas Adler and Ozan Öktem · 2018
Cited alongside, same era.
Learning a minimax optimizer: A pilot study
Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, and Zhangyang Wang · 2020
Later among the works it cites.
Denise: Deep learning based robust PCA for positive semidefinite matrices
Calypso Herrera, Florian Krach, Anastasis Kratsios, Pierre Ruyssen, and Josef Teichmann · 2020
Later among the works it cites.
Training stronger baselines for learning to optimize
Tianlong Chen, Weiyi Zhang, Zhou Jingyang, Shiyu Chang, Sijia Liu, Lisa Amini, and Zhangyang Wang · 2020
Later among the works it cites.
Accelerating ill-conditioned low-rank matrix estimation via scaled gradient descent
Tian Tong, Cong Ma, and Yuejie Chi · 2021
Closest in time.
Robust CUR decomposition: Theory and imaging applications
HanQin Cai, Keaton Hamm, Longxiu Huang, and Deanna Needell · 2021
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Accelerated structured alternating projections for robust spectrally sparse signal recovery
HanQin Cai, Jian-Feng Cai, Tianming Wang, and Guojian Yin · 2021
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A deep-unfolded reference-based RPCA network for video foreground-background separation
Huynh Van Luong, Boris Joukovsky, Yonina C Eldar, and Nikos Deligiannis · 2021
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Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing
Vishal Monga, Yuelong Li, and Yonina C Eldar · 2021
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Fast robust tensor principal component analysis via fiber CUR decomposition
HanQin Cai, Zehan Chao, Longxiu Huang, and Deanna Needell · 2021
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Generalized low-rank plus sparse tensor estimation by fast Riemannian optimization
Jian-Feng Cai, Jingyang Li, and Dong Xia · 2021
Closest in time.