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# The Gaussian Process Latent Variable Model

Neil D. Lawrence , 2006.

#### 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.

#### Cite this Paper

BibTeX

```
@InProceedings{pmlr-v-lawrence-gplvmtut06,
title = {The Gaussian Process Latent Variable Model},
author = {Neil D. Lawrence},
year = {},
editor = {},
number = {CS-06-03},
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.}
}
```

Endnote

```
%0 Conference Paper
%T The Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%B
%C Proceedings of Machine Learning Research
%D
%E
%F pmlr-v-lawrence-gplvmtut06
%I PMLR
%J Proceedings of Machine Learning Research
%P --
%U http://inverseprobability.com
%V
%N CS-06-03
%W PMLR
%X 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.
```

RIS

```
TY - CPAPER
TI - The Gaussian Process Latent Variable Model
AU - Neil D. Lawrence
BT -
PY -
DA -
ED -
ID - pmlr-v-lawrence-gplvmtut06
PB - PMLR
SP -
DP - PMLR
EP -
L1 -
UR - http://inverseprobability.com/publications/lawrence-gplvmtut06.html
AB - 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.
ER -
```

APA

`Lawrence, N.D.. (). The Gaussian Process Latent Variable Model. `*, in PMLR* (CS-06-03):-

#### Related Material

BibTeX

```
@InProceedings{/lawrence-gplvmtut06,
title = {The Gaussian Process Latent Variable Model},
author = {Neil D. Lawrence},
year = {},
editor = {},
number = {CS-06-03},
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.}
}
```

Endnote

```
%0 Conference Paper
%T The Gaussian Process Latent Variable Model
%A Neil D. Lawrence
%B
%C Proceedings of Machine Learning Research
%D
%E
%F /lawrence-gplvmtut06
%I PMLR
%J Proceedings of Machine Learning Research
%P --
%U http://inverseprobability.com
%V
%N CS-06-03
%W PMLR
%X 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.
```

RIS

```
TY - CPAPER
TI - The Gaussian Process Latent Variable Model
AU - Neil D. Lawrence
BT -
PY -
DA -
ED -
ID - /lawrence-gplvmtut06
PB - PMLR
SP -
DP - PMLR
EP -
L1 -
UR - http://inverseprobability.com/publications/lawrence-gplvmtut06.html
AB - 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.
ER -
```

APA

`Lawrence, N.D.. (). The Gaussian Process Latent Variable Model. `*, in PMLR* (CS-06-03):-