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Estimating the rank of a corrupted data matrix is an important task in data analysis, most notably for choosing the number of components in PCA.
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Vladimir A Marčenko and Leonid Andreevich Pastur · 1967
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Richard Sinkhorn · 1967
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Richard Sinkhorn · 1974
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Model averaging and dimension selection for the singular value decomposition
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Debashis Paul · 2007
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The sinkhorn–knopp algorithm: convergence and applications
Philip A Knight · 2008
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Determining the number of components in a factor model from limited noisy data
Shira Kritchman and Boaz Nadler · 2008
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Finite sample approximation results for principal component analysis: A matrix perturbation approach
Boaz Nadler et al · 2008
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Asymptotic performance of pca for high-dimensional heteroscedastic data
David Hong, Laura Balzano, and Jeffrey A Fessler · 2018
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Optimally weighted pca for high-dimensional heteroscedastic data
David Hong, Jeffrey A Fessler, and Laura Balzano · 2018
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Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex
Sinisa Hrvatin, Daniel R Hochbaum, M Aurel Nagy, Marcelo Cicconet, Keiramarie Robertson, Lucas Cheadle, Rapolas Zilionis, Alex Ratner, Rebeca Borges-Monroy, Allon M Klein, et al · 2018
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Genome-wide analysis reveals no evidence of trans chromosomal regulation of mammalian immune development
Timothy M Johanson, Hannah D Coughlan, Aaron TL Lun, Naiara G Bediaga, Gaetano Naselli, Alexandra L Garnham, Leonard C Harrison, Gordon K Smyth, and Rhys S Allan · 2018
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Pca in high dimensions: An orientation
Iain M Johnstone and Debashis Paul · 2018
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Bi-cross-validation of the svd and the nonnegative matrix factorization
Art B Owen, Patrick O Perry, et al · 2009
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Spectral analysis of large dimensional random matrices
Zhidong Bai and Jack W Silverstein · 2010
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The eigenvalues and eigenvectors of finite, low rank perturbations of large random matrices
Florent Benaych-Georges and Raj Rao Nadakuditi · 2011
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Principal component analysis for big data
Jianqing Fan, Qiang Sun, Wen-Xin Zhou, and Ziwei Zhu · 2014
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The optimal hard threshold for singular values is
Matan Gavish and David L Donoho · 2014
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Biproportional scaling of matrices and the iterative proportional fitting procedure
Friedrich Pukelsheim · 2014
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Optimal spectral shrinkage and pca with heteroscedastic noise
William Leeb and Elad Romanov · 2018
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e e pca: high dimensional exponential family pca
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Heteroskedastic pca: Algorithm, optimality, and applications
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Subspace estimation from unbalanced and incomplete data matrices:
Changxiao Cai, Gen Li, Yuejie Chi, H Vincent Poor, and Yuxin Chen · 2019
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Deterministic parallel analysis: an improved method for selecting factors and principal components
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Normalization and variance stabilization of single-cell rna-seq data using regularized negative binomial regression
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Low-rank matrix completion and denoising under poisson noise
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Low-rank model with covariates for count data with missing values
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Low-rank matrix denoising for count data using unbiased kullback-leibler risk estimation
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Better and simpler error analysis of the sinkhorn–knopp algorithm for matrix scaling
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Permutation methods for factor analysis and pca
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Screenot: Exact mse-optimal singular value thresholding in correlated noise
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Estimating number of factors by adjusted eigenvalues thresholding
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Zheng Tracy Ke, Yucong Ma, and Xihong Lin · 2020
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Scaling positive random matrices: concentration and asymptotic convergence
Boris Landa · 2020
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Heppcat: Probabilistic pca for data with heteroscedastic noise
David Hong, Kyle Gilman, Laura Balzano, and Jeffrey A Fessler · 2021
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Separating measurement and expression models clarifies confusion in single-cell rna sequencing analysis
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