Grassmann Averages for Scalable Robust PCA
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[1] Stuart Geman,et al. Statistical methods for tomographic image reconstruction , 1987 .
[2] Chris H. Q. Ding,et al. R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization , 2006, ICML.
[3] David J. Kriegman,et al. From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[4] René Vidal,et al. Distributed computer vision algorithms through distributed averaging , 2011, CVPR 2011.
[5] Yi Ma,et al. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices , 2010, Journal of structural biology.
[6] Peter J. Huber,et al. Robust Statistics , 2005, Wiley Series in Probability and Statistics.
[7] Zhixun Su,et al. Solving Principal Component Pursuit in Linear Time via $l_1$ Filtering , 2011, ArXiv.
[8] N. Campbell. Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation , 1980 .
[9] M. Bridson,et al. Metric Spaces of Non-Positive Curvature , 1999 .
[10] Michael J. Black,et al. A Framework for Robust Subspace Learning , 2003, International Journal of Computer Vision.
[11] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[12] Nojun Kwak,et al. Principal Component Analysis Based on L1-Norm Maximization , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] B. Ripley,et al. Robust Statistics , 2018, Wiley Series in Probability and Statistics.
[14] Zuowei Shen,et al. Robust Video Restoration by Joint Sparse and Low Rank Matrix Approximation , 2011, SIAM J. Imaging Sci..
[15] Ronen Basri,et al. Lambertian reflectance and linear subspaces , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[16] Gene H. Golub,et al. Matrix computations , 1983 .
[17] Shuicheng Yan,et al. Robust PCA in High-dimension: A Deterministic Approach , 2012, ICML.
[18] G. Sapiro,et al. A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography. , 2013, Journal of structural biology.
[19] Alan L. Yuille,et al. Robust principal component analysis by self-organizing rules based on statistical physics approach , 1995, IEEE Trans. Neural Networks.
[20] Michael J. Black,et al. EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation , 1996, International Journal of Computer Vision.
[21] Sam T. Roweis,et al. EM Algorithms for PCA and SPCA , 1997, NIPS.
[22] Qi Tian,et al. Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.
[23] Jian Dong,et al. Accelerated low-rank visual recovery by random projection , 2011, CVPR 2011.
[24] Hossein Hassani,et al. On the Folded Normal Distribution , 2014, 1402.3559.
[25] John M. Lee. Introduction to Smooth Manifolds , 2002 .
[26] Ameet Talwalkar,et al. Divide-and-Conquer Matrix Factorization , 2011, NIPS.
[27] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[28] Yi Ma,et al. Robust principal component analysis? , 2009, JACM.
[29] Nathan Srebro,et al. Stochastic optimization for PCA and PLS , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[30] Robin Sibson,et al. What is projection pursuit , 1987 .
[31] Xin-She Yang,et al. Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.