Proves approximate Gaussianity of debiased linear forms of eigenvectors in matrix denoising and spiked PCA models under Gaussian noise, then constructs bias/variance estimators yielding minimax-optimal confidence intervals without sample splitting.
An \ ell\_p\ theory of PCA and spectral clustering
2 Pith papers cite this work. Polarity classification is still indexing.
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Develops a toolbox for two-to-infinity norm bounds on eigenvector deviations under multiple assumption sets and derives generic conditions for perfect clustering
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Statistical Inference for Linear Functions of Eigenvectors with Small Eigengaps
Proves approximate Gaussianity of debiased linear forms of eigenvectors in matrix denoising and spiked PCA models under Gaussian noise, then constructs bias/variance estimators yielding minimax-optimal confidence intervals without sample splitting.
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Davis-Kahan Theorem in the two-to-infinity norm and its application to perfect clustering
Develops a toolbox for two-to-infinity norm bounds on eigenvector deviations under multiple assumption sets and derives generic conditions for perfect clustering