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# Large Scale Learning with the Gaussian Process Latent Variable Model

Neil D. Lawrence , 2006.

#### 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.

#### Cite this Paper

BibTeX

```
@InProceedings{pmlr-v-lawrence-largescale06,
title = {Large Scale Learning with the Gaussian Process Latent Variable Model},
author = {Neil D. Lawrence},
year = {},
editor = {},
number = {CS-06-05},
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.}
}
```

Endnote

```
%0 Conference Paper
%T Large Scale Learning with the Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%B
%C Proceedings of Machine Learning Research
%D
%E
%F pmlr-v-lawrence-largescale06
%I PMLR
%J Proceedings of Machine Learning Research
%P --
%U http://inverseprobability.com
%V
%N CS-06-05
%W PMLR
%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.
```

RIS

```
TY - CPAPER
TI - Large Scale Learning with the Gaussian Process Latent Variable Model
AU - Neil D. Lawrence
BT -
PY -
DA -
ED -
ID - pmlr-v-lawrence-largescale06
PB - PMLR
SP -
DP - PMLR
EP -
L1 -
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 -
```

APA

`Lawrence, N.D.. (). Large Scale Learning with the Gaussian Process Latent Variable Model. `*, in PMLR* (CS-06-05):-

#### Related Material

BibTeX

```
@InProceedings{/lawrence-largescale06,
title = {Large Scale Learning with the Gaussian Process Latent Variable Model},
author = {Neil D. Lawrence},
year = {},
editor = {},
number = {CS-06-05},
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.}
}
```

Endnote

```
%0 Conference Paper
%T Large Scale Learning with the Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%B
%C Proceedings of Machine Learning Research
%D
%E
%F /lawrence-largescale06
%I PMLR
%J Proceedings of Machine Learning Research
%P --
%U http://inverseprobability.com
%V
%N CS-06-05
%W PMLR
%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.
```

RIS

```
TY - CPAPER
TI - Large Scale Learning with the Gaussian Process Latent Variable Model
AU - Neil D. Lawrence
BT -
PY -
DA -
ED -
ID - /lawrence-largescale06
PB - PMLR
SP -
DP - PMLR
EP -
L1 -
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 -
```

APA

`Lawrence, N.D.. (). Large Scale Learning with the Gaussian Process Latent Variable Model. `*, in PMLR* (CS-06-05):-