The Gaussian Process Latent Variable Model

[edit]

Neil D. Lawrence, University of Sheffield

Related Material

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.


@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}
 

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