Pages that link to "Item:Q1000307"

The following pages link to Finite sample approximation results for principal component analysis: A matrix perturbation approach (Q1000307):

Displaying 28 items.

• Multiscale geometric methods for data sets. I: Multiscale SVD, noise and curvature. (Q2402490) (← links)
• Regression on manifolds: estimation of the exterior derivative (Q2429924) (← links)
• Minimax sparse principal subspace estimation in high dimensions (Q2443207) (← links)
• Sparse PCA: optimal rates and adaptive estimation (Q2443213) (← links)
• Random matrix theory in statistics: a review (Q2453609) (← links)
• On the principal components of sample covariance matrices (Q2634905) (← links)
• Matrix means and a novel high-dimensional shrinkage phenomenon (Q2676932) (← links)
• The Impact of Measurement Error on Principal Component Analysis (Q2932773) (← links)
• Statistical challenges of high-dimensional data (Q3559944) (← links)
• Challenges for Panel Financial Analysis (Q4558820) (← links)
• (Q5004041) (← links)
• On the distribution of an arbitrary subset of the eigenvalues for some finite dimensional random matrices (Q5108687) (← links)
• Uniform Bounds for Invariant Subspace Perturbations (Q5146696) (← links)
• Sparse Principal Component Analysis in Hilbert Space (Q5177962) (← links)
• Local Linear Regression on Manifolds and Its Geometric Interpretation (Q5406369) (← links)
• Wald Statistics in high-dimensional PCA (Q5881043) (← links)
• Biwhitening Reveals the Rank of a Count Matrix (Q5885827) (← links)
• Optimally Weighted PCA for High-Dimensional Heteroscedastic Data (Q5888296) (← links)
• Estimating structured high-dimensional covariance and precision matrices: optimal rates and adaptive estimation (Q5965313) (← links)
• A note on the prediction error of principal component regression in high dimensions (Q6050280) (← links)
• Fast randomized numerical rank estimation for numerically low-rank matrices (Q6154411) (← links)
• The limiting spectral distribution of large-dimensional general information-plus-noise-type matrices (Q6161610) (← links)
• A CLT for the LSS of large-dimensional sample covariance matrices with diverging spikes (Q6183780) (← links)
• Feature grouping and sparse principal component analysis with truncated regularization (Q6548777) (← links)
• Inference for heteroskedastic PCA with missing data (Q6550970) (← links)
• Exactly Uncorrelated Sparse Principal Component Analysis (Q6552546) (← links)
• Subexponential-time algorithms for sparse PCA (Q6566150) (← links)
• Testing Simultaneous Diagonalizability (Q6567946) (← links)