The Gaussian Process Latent Variable Model

Neil D. Lawrence
, 2006.

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.

Cite this Paper


BibTeX
@Misc{Lawrence:gplvmtut06, title = {The Gaussian Process Latent Variable Model}, author = {Neil D. Lawrence}, year = {2006}, number = {CS-06-03}, 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.} }
Endnote
%0 Generic %T The Gaussian Process Latent Variable Model %A Neil D. Lawrence %D 2006 %F Lawrence:gplvmtut06 %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.
RIS
TY - GEN TI - The Gaussian Process Latent Variable Model AU - Neil D. Lawrence DA - 2006/01/01 ID - Lawrence:gplvmtut06 IS - CS-06-03 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 -
APA
Lawrence, N.D.. (2006). The Gaussian Process Latent Variable Model. (CS-06-03)

Related Material