<--- Back to Details
First PageDocument Content
Estimation theory / Singular value decomposition / Statistical models / Principal component analysis / Expectation–maximization algorithm / Factor analysis / Mixture model / Eigenvalues and eigenvectors / Covariance matrix / Statistics / Multivariate statistics / Data analysis
Date: 2006-01-18 10:11:21
Estimation theory
Singular value decomposition
Statistical models
Principal component analysis
Expectation–maximization algorithm
Factor analysis
Mixture model
Eigenvalues and eigenvectors
Covariance matrix
Statistics
Multivariate statistics
Data analysis

Add to Reading List

Source URL: www.robots.ox.ac.uk

Download Document from Source Website

File Size: 111,49 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

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

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

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

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

DocID: 1rHEk - View Document