Hierarchical Gaussian Process Latent Variable Models

Neil D. Lawrence, Andrew J. Moore
,  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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-lawrence-hgplvm07, title = {Hierarchical Gaussian Process Latent Variable Models}, author = {Neil D. Lawrence and Andrew J. Moore}, pages = {481--488}, year = {}, editor = {}, volume = {24}, url = {http://inverseprobability.com/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 %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-hgplvm07 %I PMLR %J Proceedings of Machine Learning Research %P 481--488 %U http://inverseprobability.com %V %W PMLR %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 - PY - DA - ED - ID - pmlr-v-lawrence-hgplvm07 PB - PMLR SP - 481 DP - PMLR EP - 488 L1 - UR - http://inverseprobability.com/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.. (). Hierarchical Gaussian Process Latent Variable Models. , in PMLR :481-488

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