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Optimal Shrinkage of Eigenvalues in the Spiked Covariance Model

4 November 2013

Papers citing "Optimal Shrinkage of Eigenvalues in the Spiked Covariance Model"

39 / 89 papers shown

Title
How to reduce dimension with PCA and random projections? Fan Yang Sifan Liu Yan Sun David P. Woodruff 63 28 0 01 May 2020
Regularization in High-Dimensional Regression and Classification via Random Matrix Theory Panagiotis Lolas 84 14 0 30 Mar 2020
Geodesically parameterized covariance estimation A. Musolas S.T. Smith Youssef Marzouk 59 4 0 06 Jan 2020
A note on identifiability conditions in confirmatory factor analysis W. Leeb CML 33 4 0 05 Dec 2019
Matrix Means and a Novel High-Dimensional Shrinkage Phenomenon A. Lodhia Keith D. Levin Elizaveta Levina 30 3 0 16 Oct 2019
Asymptotics of empirical eigenvalues for large separable covariance matrices Tiebin Mi Robert C. Qiu 67 0 0 10 Oct 2019
Spiked separable covariance matrices and principal components Xiucai Ding Fan Yang 87 57 0 29 May 2019
Rapid evaluation of the spectral signal detection threshold and Stieltjes transform W. Leeb 91 7 0 26 Apr 2019
Optimal Recovery of Precision Matrix for Mahalanobis Distance from High Dimensional Noisy Observations in Manifold Learning M. Gavish Ronen Talmon P. Su Hau‐Tieng Wu 47 8 0 19 Apr 2019
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization Qilong Wang Jiangtao Xie W. Zuo Lei Zhang P. Li 81 99 0 15 Apr 2019
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification L. Duan 58 9 0 21 Mar 2019
Matrix denoising for weighted loss functions and heterogeneous signals W. Leeb 87 25 0 25 Feb 2019
Statistical inference with F-statistics when fitting simple models to high-dimensional data Hannes Leeb Lukas Steinberger 24 1 0 12 Feb 2019
Robust Streaming PCA D. Bienstock Minchan Jeong Apurv Shukla Se-Young Yun 42 3 0 08 Feb 2019
Steerable $e$ PCA: Rotationally Invariant Exponential Family PCA Zhizhen Zhao Lydia T. Liu A. Singer 47 3 0 20 Dec 2018
Optimal spectral shrinkage and PCA with heteroscedastic noise Qiangqiang Wu Yanjie Liang 81 25 0 06 Nov 2018
RSVP-graphs: Fast High-dimensional Covariance Matrix Estimation under Latent Confounding Rajen Dinesh Shah Benjamin Frot Gian-Andrea Thanei N. Meinshausen 68 6 0 02 Nov 2018
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data David Hong Fan Yang Jeffrey A. Fessler Laura Balzano 107 26 0 30 Oct 2018
Heteroskedastic PCA: Algorithm, Optimality, and Applications Anru R. Zhang T. Tony Cai Yihong Wu 197 72 0 19 Oct 2018
Optimal Covariance Estimation for Condition Number Loss in the Spiked Model D. Donoho Behrooz Ghorbani 141 7 0 17 Oct 2018
Adapting to Unknown Noise Distribution in Matrix Denoising Andrea Montanari Feng Ruan Jun Yan 100 13 0 06 Oct 2018
Harmonic Alignment Jay S. Stanley Scott A. Gigante Guy Wolf Smita Krishnaswamy DiffM 46 16 0 30 Sep 2018
Robust high dimensional factor models with applications to statistical machine learning Jianqing Fan Kaizheng Wang Yiqiao Zhong Ziwei Zhu 86 55 0 12 Aug 2018
Estimating Learnability in the Sublinear Data Regime Weihao Kong Gregory Valiant 102 30 0 04 May 2018
Graph-based regularization for regression problems with alignment and highly-correlated designs Yuan Li Benjamin Mark Garvesh Raskutti Rebecca Willett Hyebin Song David Neiman 42 23 0 20 Mar 2018
Multidimensional Scaling of Noisy High Dimensional Data Erez Peterfreund M. Gavish 42 22 0 30 Jan 2018
The Dispersion Bias L. Goldberg A. Papanicolaou Alexander D. Shkolnik 72 16 0 15 Nov 2017
Tail sums of Wishart and GUE eigenvalues beyond the bulk edge Iain M. Johnstone 61 1 0 21 Apr 2017
Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals A. Beskos Ajay Jasra K. Law Youssef Marzouk Yan Zhou 67 42 0 15 Mar 2017
Think globally, fit locally under the Manifold Setup: Asymptotic Analysis of Locally Linear Embedding Hau‐Tieng Wu Nan Wu 51 40 0 12 Mar 2017
PCA from noisy, linearly reduced data: the diagonal case Edgar Dobriban W. Leeb A. Singer 86 5 0 30 Nov 2016
Graph-Guided Banding of the Covariance Matrix Jacob Bien 61 6 0 01 Jun 2016
Denoising and Covariance Estimation of Single Particle Cryo-EM Images T. Bhamre Teng Zhang A. Singer 53 65 0 22 Feb 2016
Latent common manifold learning with alternating diffusion: analysis and applications Ronen Talmon Hau‐Tieng Wu MedIm 74 44 0 30 Jan 2016
Spectrum Estimation from Samples Weihao Kong Gregory Valiant 117 73 0 30 Jan 2016
Newton-Stein Method: An optimization method for GLMs via Stein's Lemma Murat A. Erdogdu 63 13 0 28 Nov 2015
Towards Effective Codebookless Model for Image Classification Qilong Wang P. Li Lei Zhang W. Zuo 81 31 0 09 Jul 2015
Fast Steerable Principal Component Analysis Zhizhen Zhao Y. Shkolnisky A. Singer 74 70 0 02 Dec 2014
Optimal Shrinkage of Singular Values M. Gavish D. Donoho 153 183 0 29 May 2014