<--- Back to Details
First PageDocument Content
Matrix theory / Covariance and correlation / Singular value decomposition / Numerical linear algebra / Matrix / Eigenvalues and eigenvectors / Correlation and dependence / Moore–Penrose pseudoinverse / Algebra / Linear algebra / Mathematics
Date: 2011-07-26 17:45:44
Matrix theory
Covariance and correlation
Singular value decomposition
Numerical linear algebra
Matrix
Eigenvalues and eigenvectors
Correlation and dependence
Moore–Penrose pseudoinverse
Algebra
Linear algebra
Mathematics

Splitting the core Peter J¨ackel∗ First version: This version: 10th October 2004

Add to Reading List

Source URL: www.pjaeckel.webspace.virginmedia.com

Download Document from Source Website

File Size: 88,56 KB

Share Document on Facebook

Similar Documents

Stat 991: Multivariate Analysis, Dimensionality Reduction, and Spectral Methods  Lecture: 1 The Singular Value Decomposition Instructor: Sham Kakade

Stat 991: Multivariate Analysis, Dimensionality Reduction, and Spectral Methods Lecture: 1 The Singular Value Decomposition Instructor: Sham Kakade

DocID: 1vbS6 - View Document

Sampling Algorithms to Update Truncated SVD Ichitaro Yamazaki, Stanimire Tomov, and Jack Dongarra University of Tennessee, Knoxville, Tennessee, U.S.A. Abstract— A truncated singular value decomposition (SVD) is a powe

Sampling Algorithms to Update Truncated SVD Ichitaro Yamazaki, Stanimire Tomov, and Jack Dongarra University of Tennessee, Knoxville, Tennessee, U.S.A. Abstract— A truncated singular value decomposition (SVD) is a powe

DocID: 1udU9 - View Document

Orthogonal Matrices and the Singular Value Decomposition Carlo Tomasi The first Section below extends to m × n matrices the results on orthogonality and projection we have previously seen for vectors. The Sections there

Orthogonal Matrices and the Singular Value Decomposition Carlo Tomasi The first Section below extends to m × n matrices the results on orthogonality and projection we have previously seen for vectors. The Sections there

DocID: 1tr0F - View Document

Using Singular Value Decomposition to Parameterize State-Dependent Model Errors Christopher M. Danforth∗ Department of Mathematics and Statistics, University of Vermont Burlington, VTEugenia Kalnay

Using Singular Value Decomposition to Parameterize State-Dependent Model Errors Christopher M. Danforth∗ Department of Mathematics and Statistics, University of Vermont Burlington, VTEugenia Kalnay

DocID: 1tjbW - View Document

CS168: The Modern Algorithmic Toolbox Lecture #9: The Singular Value Decomposition (SVD) and Low-Rank Matrix Approximations Tim Roughgarden & Gregory Valiant∗ April 25, 2016

CS168: The Modern Algorithmic Toolbox Lecture #9: The Singular Value Decomposition (SVD) and Low-Rank Matrix Approximations Tim Roughgarden & Gregory Valiant∗ April 25, 2016

DocID: 1rHEk - View Document