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On the principal components of sample covariance matrices
v1v2v3 (latest)

On the principal components of sample covariance matrices

3 April 2014
Alex Bloemendal
Antti Knowles
H. Yau
J. Yin
ArXiv (abs)PDFHTML

Papers citing "On the principal components of sample covariance matrices"

15 / 65 papers shown
Title
Statistical thresholds for Tensor PCA
Statistical thresholds for Tensor PCA
Aukosh Jagannath
P. Lopatto
Léo Miolane
54
44
0
08 Dec 2018
Notes on asymptotics of sample eigenstructure for spiked covariance
  models with non-Gaussian data
Notes on asymptotics of sample eigenstructure for spiked covariance models with non-Gaussian data
Iain M. Johnstone
Jeha Yang
22
5
0
24 Oct 2018
Asymptotics of eigenstructure of sample correlation matrices for
  high-dimensional spiked models
Asymptotics of eigenstructure of sample correlation matrices for high-dimensional spiked models
D. Morales-Jiménez
Iain M. Johnstone
M. Mckay
Jeha Yang
86
30
0
24 Oct 2018
Modified Multidimensional Scaling and High Dimensional Clustering
Xiucai Ding
Qiang Sun
73
4
0
24 Oct 2018
Adapting to Unknown Noise Distribution in Matrix Denoising
Adapting to Unknown Noise Distribution in Matrix Denoising
Andrea Montanari
Feng Ruan
Jun Yan
100
13
0
06 Oct 2018
Singular vector and singular subspace distribution for the matrix
  denoising model
Singular vector and singular subspace distribution for the matrix denoising model
Z. Bao
Xiucai Ding
Ke Wang
111
51
0
27 Sep 2018
Matrices with Gaussian noise: optimal estimates for singular subspace
  perturbation
Matrices with Gaussian noise: optimal estimates for singular subspace perturbation
Sean O’Rourke
Van Vu
Ke Wang
81
7
0
02 Mar 2018
Limiting Laws for Divergent Spiked Eigenvalues and Largest Non-spiked
  Eigenvalue of Sample Covariance Matrices
Limiting Laws for Divergent Spiked Eigenvalues and Largest Non-spiked Eigenvalue of Sample Covariance Matrices
Tony Cai
Xiao Han
G. Pan
69
80
0
01 Nov 2017
Efficient Estimation of Linear Functionals of Principal Components
Efficient Estimation of Linear Functionals of Principal Components
V. Koltchinskii
Matthias Loffler
Richard Nickl
66
33
0
25 Aug 2017
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
Levent Sagun
Utku Evci
V. U. Güney
Yann N. Dauphin
Léon Bottou
98
420
0
14 Jun 2017
High dimensional deformed rectangular matrices with applications in
  matrix denoising
High dimensional deformed rectangular matrices with applications in matrix denoising
Xiucai Ding
100
46
0
22 Feb 2017
Cleaning large correlation matrices: tools from random matrix theory
Cleaning large correlation matrices: tools from random matrix theory
J. Bun
J. Bouchaud
M. Potters
82
268
0
25 Oct 2016
Universal halting times in optimization and machine learning
Universal halting times in optimization and machine learning
Levent Sagun
T. Trogdon
Yann LeCun
BDL
53
9
0
19 Nov 2015
Large complex correlated Wishart matrices: Fluctuations and asymptotic
  independence at the edges
Large complex correlated Wishart matrices: Fluctuations and asymptotic independence at the edges
W. Hachem
A. Hardy
J. Najim
175
33
0
26 Sep 2014
Tracy-Widom Distribution for the Largest Eigenvalue of Real Sample
  Covariance Matrices with General Population
Tracy-Widom Distribution for the Largest Eigenvalue of Real Sample Covariance Matrices with General Population
J. Lee
Kevin Schnelli
82
96
0
17 Sep 2014
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