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We study the linear subspace fitting problem in the overparameterized setting, where the estimated subspace can perfectly interpolate the training examples.
Two models of double descent for weak features
Belkin, M., Hsu, D., and Xu, J · 1903
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Discarding variables in a principal component analysis. i: Artificial data
Jolliffe, I. T · 1972
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Discarding variables in a principal component analysis. ii: Real data
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Closest unitary, orthogonal and Hermitian operators to a given operator
Keller, J. B · 1975
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How many variables should be entered in a regression equation?
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Procrustes Problems , volume 30
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Eigenvectors from eigenvalues: A survey of a basic identity in linear algebra
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Scaling description of generalization with number of parameters in deep learning
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Surprises in high-dimensional ridgeless least squares interpolation
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A general framework for consistency of principal component analysis
Shen, D., Shen, H., and Marron, J · 2016
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Reconciling modern machine-learning practice and the classical bias–variance trade-off
Belkin, M., Hsu, D., Ma, S., and Mandal, S
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Mei, S. and Montanari, A · 2019
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On the number of variables to use in principal component regression
Xu, J. and Hsu, D. J · 2019
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The nearest orthogonal or unitary matrix, August 2011
Kahan, W · 2020
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