# Large Scale Learning with the Gaussian Process Latent Variable Model

Neil D. Lawrence, University of Sheffield

### Errata

• Page 14: Sign and inverse wrong on definition of $\mathbf{C}$ just before (4). Corrected in version from December 16, 2008.
Thanks to: John Guiver

#### Abstract

In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GP-LVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. We briefly consider a GPR toy problem to highlight the strenghts and weaknesses of the different approaches before studying the perfomance of these techniques on a benchmark visualisation data set.

  @TechReport{lawrence-largescale06, title = {Large Scale Learning with the Gaussian Process Latent Variable Model}, author = {Neil D. Lawrence}, year = {2006}, institution = {University of Sheffield}, number = {CS-06-05}, month = {00}, edit = {https://github.com/lawrennd//publications/edit/gh-pages/_posts/2006-01-01-lawrence-largescale06.md}, url = {http://inverseprobability.com/publications/lawrence-largescale06.html}, abstract = {In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GP-LVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. We briefly consider a GPR toy problem to highlight the strenghts and weaknesses of the different approaches before studying the perfomance of these techniques on a benchmark visualisation data set.}, key = {Lawrence:largescale06}, note = {Document updated on December 16, 2008. Original from February 17th, 2006.}, linkpdf = {ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmSparse.pdf}, linksoftware = {https://github.com/SheffieldML/GPmat/}, group = {gplvm,motion,shefml,dimensional reduction} }
 %T Large Scale Learning with the Gaussian Process Latent Variable Model %A Neil D. Lawrence %B %D %F lawrence-largescale06 %P -- %R %U http://inverseprobability.com/publications/lawrence-largescale06.html %N CS-06-05 %X In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GP-LVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. We briefly consider a GPR toy problem to highlight the strenghts and weaknesses of the different approaches before studying the perfomance of these techniques on a benchmark visualisation data set. 
 TY - CPAPER TI - Large Scale Learning with the Gaussian Process Latent Variable Model AU - Neil D. Lawrence PY - 2006/01/01 DA - 2006/01/01 ID - lawrence-largescale06 SP - EP - L1 - ftp://ftp.dcs.shef.ac.uk/home/neil/gplvmSparse.pdf UR - http://inverseprobability.com/publications/lawrence-largescale06.html AB - In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GP-LVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. We briefly consider a GPR toy problem to highlight the strenghts and weaknesses of the different approaches before studying the perfomance of these techniques on a benchmark visualisation data set. ER - 
 Lawrence, N.D.. (2006). Large Scale Learning with the Gaussian Process Latent Variable Model.(CS-06-05):-