The Gaussian process latent variable model (GP-LVM) is a recently proposed probabilistic approach to obtaining a reduced dimension representation of a data set. In this tutorial we motivate and describe the GP-LVM, giving reviews of the model itself and some of the concepts behind it.

@TechReport{lawrence-gplvmtut06,
title = {The Gaussian Process Latent Variable Model},
author = {Neil D. Lawrence},
year = {2006},
institution = {The University of Sheffield, Department of Computer Science},
number = {CS-06-03},
month = {00},
edit = {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2006-01-01-lawrence-gplvmtut06.md},
url = {http://inverseprobability.com/publications/lawrence-gplvmtut06.html},
abstract = {The Gaussian process latent variable model (GP-LVM) is a recently proposed probabilistic approach to obtaining a reduced dimension representation of a data set. In this tutorial we motivate and describe the GP-LVM, giving reviews of the model itself and some of the concepts behind it.},
key = {Lawrence:gplvmtut06},
linkpdf = {ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmTutorial.pdf},
group = {shefml,gplvm,motion,dimensional reduction}
}

%T The Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%B
%D
%F lawrence-gplvmtut06
%P --
%R
%U http://inverseprobability.com/publications/lawrence-gplvmtut06.html
%N CS-06-03
%X The Gaussian process latent variable model (GP-LVM) is a recently proposed probabilistic approach to obtaining a reduced dimension representation of a data set. In this tutorial we motivate and describe the GP-LVM, giving reviews of the model itself and some of the concepts behind it.

TY - CPAPER
TI - The Gaussian Process Latent Variable Model
AU - Neil D. Lawrence
PY - 2006/01/01
DA - 2006/01/01
ID - lawrence-gplvmtut06
SP -
EP -
L1 - ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmTutorial.pdf
UR - http://inverseprobability.com/publications/lawrence-gplvmtut06.html
AB - The Gaussian process latent variable model (GP-LVM) is a recently proposed probabilistic approach to obtaining a reduced dimension representation of a data set. In this tutorial we motivate and describe the GP-LVM, giving reviews of the model itself and some of the concepts behind it.
ER -

Lawrence, N.D.. (2006). The Gaussian Process Latent Variable Model.(CS-06-03):-

@TechReport{/lawrence-gplvmtut06,
title = {The Gaussian Process Latent Variable Model},
author = {Neil D. Lawrence},
year = {2006},
institution = {The University of Sheffield, Department of Computer Science},
number = {CS-06-03},
month = {00},
edit = {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2006-01-01-lawrence-gplvmtut06.md},
url = {http://inverseprobability.com/publications/lawrence-gplvmtut06.html},
abstract = {The Gaussian process latent variable model (GP-LVM) is a recently proposed probabilistic approach to obtaining a reduced dimension representation of a data set. In this tutorial we motivate and describe the GP-LVM, giving reviews of the model itself and some of the concepts behind it.},
key = {Lawrence:gplvmtut06},
linkpdf = {ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmTutorial.pdf},
group = {shefml,gplvm,motion,dimensional reduction}
}

%T The Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%B
%D
%F /lawrence-gplvmtut06
%P --
%R
%U http://inverseprobability.com/publications/lawrence-gplvmtut06.html
%N CS-06-03
%X The Gaussian process latent variable model (GP-LVM) is a recently proposed probabilistic approach to obtaining a reduced dimension representation of a data set. In this tutorial we motivate and describe the GP-LVM, giving reviews of the model itself and some of the concepts behind it.

TY - CPAPER
TI - The Gaussian Process Latent Variable Model
AU - Neil D. Lawrence
PY - 2006/01/01
DA - 2006/01/01
ID - /lawrence-gplvmtut06
SP -
EP -
L1 - ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmTutorial.pdf
UR - http://inverseprobability.com/publications/lawrence-gplvmtut06.html
AB - The Gaussian process latent variable model (GP-LVM) is a recently proposed probabilistic approach to obtaining a reduced dimension representation of a data set. In this tutorial we motivate and describe the GP-LVM, giving reviews of the model itself and some of the concepts behind it.
ER -

Lawrence, N.D.. (2006). The Gaussian Process Latent Variable Model.(CS-06-03):-