Hierarchical Gaussian Process Latent Variable Models

Neil D. Lawrence; Andrew J. Moore

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Hierarchical Gaussian Process Latent Variable Models

Neil D. Lawrence, Andrew J. Moore

Proceedings of the International Conference in Machine Learning, Omnipress 24:481-488, 2007.

Abstract

The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we extend the GP-LVM through hierarchies. A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure. We first introduce Gaussian process hierarchies through a simple dynamical model, we then extend the approach to a more complex hierarchy which is applied to the visualisation of human motion data sets.

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BibTeX


@InProceedings{Lawrence:hgplvm07,
  title = 	 {Hierarchical {G}aussian Process Latent Variable Models},
  author = 	 {Lawrence, Neil D. and Moore, Andrew J.},
  booktitle = 	 {Proceedings of the International Conference in Machine Learning},
  pages = 	 {481--488},
  year = 	 {2007},
  editor = 	 {Ghahramani, Zoubin},
  volume = 	 {24},
  publisher =    {Omnipress},
  pdf = 	 {https://icml.cc/imls/conferences/2007/proceedings/papers/408.pdf},
  url = 	 {/publications/lawrence-hgplvm07.html},
  abstract = 	 {The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we extend the GP-LVM through hierarchies. A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure. We first introduce Gaussian process hierarchies through a simple dynamical model, we then extend the approach to a more complex hierarchy which is applied to the visualisation of human motion data sets.}
}

Endnote

%0 Conference Paper
%T Hierarchical Gaussian Process Latent Variable Models
%A Neil D. Lawrence
%A Andrew J. Moore
%B Proceedings of the International Conference in Machine Learning
%D 2007
%E Zoubin Ghahramani	
%F Lawrence:hgplvm07
%I Omnipress
%P 481--488
%U /publications/lawrence-hgplvm07.html
%V 24
%X The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we extend the GP-LVM through hierarchies. A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure. We first introduce Gaussian process hierarchies through a simple dynamical model, we then extend the approach to a more complex hierarchy which is applied to the visualisation of human motion data sets.

RIS


TY  - CPAPER
TI  - Hierarchical Gaussian Process Latent Variable Models
AU  - Neil D. Lawrence
AU  - Andrew J. Moore
BT  - Proceedings of the International Conference in Machine Learning
DA  - 2007/01/01
ED  - Zoubin Ghahramani	
ID  - Lawrence:hgplvm07
PB  - Omnipress
VL  - 24
SP  - 481
EP  - 488
L1  - https://icml.cc/imls/conferences/2007/proceedings/papers/408.pdf
UR  - /publications/lawrence-hgplvm07.html
AB  - The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we extend the GP-LVM through hierarchies. A hierarchical model (such as a tree) allows us to express conditional independencies in the data as well as the manifold structure. We first introduce Gaussian process hierarchies through a simple dynamical model, we then extend the approach to a more complex hierarchy which is applied to the visualisation of human motion data sets.
ER  -

APA


Lawrence, N.D. & Moore, A.J.. (2007). Hierarchical Gaussian Process Latent Variable Models. Proceedings of the International Conference in Machine Learning 24:481-488 Available from /publications/lawrence-hgplvm07.html.