<--- Back to Details
First PageDocument Content
Machine learning / Singular value decomposition / Data analysis / Linear algebra / Principal component analysis / Expectation–maximization algorithm / Factor analysis / Mixture model / Dimension reduction / Statistics / Algebra / Multivariate statistics
Date: 2006-06-26 08:01:05
Machine learning
Singular value decomposition
Data analysis
Linear algebra
Principal component analysis
Expectation–maximization algorithm
Factor analysis
Mixture model
Dimension reduction
Statistics
Algebra
Multivariate statistics

Add to Reading List

Source URL: www.miketipping.com

Download Document from Source Website

File Size: 547,50 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