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In statistics and machine learning, people are often interested in the eigenvectors (or singular vectors) of certain matrices (e.g.
Spectral clustering and the high-dimensional stochastic blockmodel
Rohe, K · 1915
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Symmetric gauge functions and unitarily invariant norms
Mirsky, L · 1960
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Robust estimation of a location parameter
Huber, P. J · 1964
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The rotation of eigenvectors by a perturbation. iii
Davis, C · 1970
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Perturbation bounds in connection with singular value decomposition
Wedin, P.-Å · 1972
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Arbitrage, factor structure, and mean-variance analysis on large asset markets
Chamberlain, G · 1982
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Symmetric multivariate and related distributions
Fang, K.-T · 1990
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Matrix perturbation theory
Stewart, G. W · 1990
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Common risk factors in the returns on stocks and bonds
Fama, E. F · 1993
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A multivariate version of kendall’s τ \tau
Choi, K · 1998
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A bayesian computer vision system for modeling human interactions
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Regularization of wavelet approximations
Antoniadis, A · 2001
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Convex position estimation in wireless sensor networks
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Principal component analysis
Jolliffe, I · 2002
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On spectral clustering: Analysis and an algorithm
Ng, A. Y · 2002
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Completely bounded maps and operator algebras
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Forecasting using principal components from a large number of predictors
Stock, J · 2002
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Inferential theory for factor models of large dimensions
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Semidefinite programming for ad hoc wireless sensor network localization
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Hu, W · 2004
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Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
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Challenging the empirical mean and empirical variance: a deviation study
Catoni, O · 2012
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User-friendly tail bounds for sums of random matrices
Tropp, J. A · 2012
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Minimax rates of estimation for sparse pca in high dimensions
Vu, V. Q · 2012
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Optimal detection of sparse principal components in high dimension
Berthet, Q · 2013
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Large covariance estimation by thresholding principal orthogonal complements
Fan, J · 2013
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Elliptically contoured models in statistics and portfolio theory
Gupta, A. K · 2013
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