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Principal Component Analysis (PCA) is a powerful tool in statistics and machine learning.
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Statistical properties of kernel principal component analysis
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Finite sample approximation results for principal component analysis: A matrix perturbation approach
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Genes mirror geography within Europe
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What is principal component analysis?
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Exact matrix completion via convex optimization
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Clustering subgaussian mixtures by semidefinite programming
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Asymptotics of empirical eigenstructure for high dimensional spiked covariance
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Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics
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Contextual stochastic block models
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Sharp optimal recovery in the two component Gaussian mixture model
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Random perturbation of low rank matrices: Improving classical bounds
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The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
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Spectral method and regularized MLE are both optimal for top-K ranking
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An ℓ ∞ \ell_{\infty} eigenvector perturbation bound and its application to robust covariance estimation
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Partial recovery bounds for clustering with the relaxed k k -means
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Entrywise eigenvector analysis of random matrices with low expected rank
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