Convergence of Sample Eigenvalues, Eigenvectors, and Principal Component Scores for Ultra-High Dimensional Data

Biometrika - United Kingdom
doi 10.1093/biomet/ast064
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Oxford University Press (OUP)

Related search

Principal Component Analysis for Sparse High-Dimensional Data

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
English

Spiked Eigenvalues of High-Dimensional Separable Sample Covariance Matrices

SSRN Electronic Journal
2019English

Spatially Weighted Principal Component Regression for High-Dimensional Prediction

Lecture Notes in Computer Science
Computer ScienceTheoretical Computer Science
2015English

MWPCR: Multiscale Weighted Principal Component Regression for High-Dimensional Prediction

Journal of the American Statistical Association
UncertaintyStatisticsProbability
2016English

High Dimensional Semiparametric Scale-Invariant Principal Component Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern RecognitionApplied MathematicsComputer VisionMathematicsComputational TheoryArtificial IntelligenceSoftware
2014English

Eigenvalues and Eigenvectors for a G-Frame Operator

Hacettepe Journal of Mathematics and Statistics
2019English

High-Dimensional Data Classification Based on Principal Component Analysis Dimension Reduction and Improved BP Algorithm

DEStech Transactions on Computer Science and Engineering
2018English

Dimensionality Reduction of High-Dimensional Data With a NonLinear Principal Component Aligned Generative Topographic Mapping

SIAM Journal of Scientific Computing
Computational MathematicsApplied Mathematics
2014English

The Effect of Principal Component Analysis on Machine Learning Accuracy With High Dimensional Spectral Data

English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy