Learning a Parametric Embedding by Preserving Local Structure

Laurens van der Maaten
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:384-391, 2009.

Abstract

The paper presents a new unsupervised dimensionality reduction technique, called parametric t-SNE, that learns a parametric mapping between the high-dimensional data space and the low-dimensional latent space. Parametric t-SNE learns the parametric mapping in such a way that the local structure of the data is preserved as well as possible in the latent space. We evaluate the performance of parametric t-SNE in experiments on two datasets, in which we compare it to the performance of two other unsupervised parametric dimensionality reduction techniques. The results of experiments illustrate the strong performance of parametric t-SNE, in particular, in learning settings in which the dimensionality of the latent space is relatively low.

Cite this Paper


BibTeX
@InProceedings{pmlr-v5-maaten09a, title = {Learning a Parametric Embedding by Preserving Local Structure}, author = {Laurens van der Maaten}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {384--391}, year = {2009}, editor = {David van Dyk and Max Welling}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/maaten09a/maaten09a.pdf}, url = {http://proceedings.mlr.press/v5/maaten09a.html}, abstract = {The paper presents a new unsupervised dimensionality reduction technique, called parametric t-SNE, that learns a parametric mapping between the high-dimensional data space and the low-dimensional latent space. Parametric t-SNE learns the parametric mapping in such a way that the local structure of the data is preserved as well as possible in the latent space. We evaluate the performance of parametric t-SNE in experiments on two datasets, in which we compare it to the performance of two other unsupervised parametric dimensionality reduction techniques. The results of experiments illustrate the strong performance of parametric t-SNE, in particular, in learning settings in which the dimensionality of the latent space is relatively low.} }
Endnote
%0 Conference Paper %T Learning a Parametric Embedding by Preserving Local Structure %A Laurens van der Maaten %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-maaten09a %I PMLR %J Proceedings of Machine Learning Research %P 384--391 %U http://proceedings.mlr.press %V 5 %W PMLR %X The paper presents a new unsupervised dimensionality reduction technique, called parametric t-SNE, that learns a parametric mapping between the high-dimensional data space and the low-dimensional latent space. Parametric t-SNE learns the parametric mapping in such a way that the local structure of the data is preserved as well as possible in the latent space. We evaluate the performance of parametric t-SNE in experiments on two datasets, in which we compare it to the performance of two other unsupervised parametric dimensionality reduction techniques. The results of experiments illustrate the strong performance of parametric t-SNE, in particular, in learning settings in which the dimensionality of the latent space is relatively low.
RIS
TY - CPAPER TI - Learning a Parametric Embedding by Preserving Local Structure AU - Laurens van der Maaten BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics PY - 2009/04/15 DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-maaten09a PB - PMLR SP - 384 DP - PMLR EP - 391 L1 - http://proceedings.mlr.press/v5/maaten09a/maaten09a.pdf UR - http://proceedings.mlr.press/v5/maaten09a.html AB - The paper presents a new unsupervised dimensionality reduction technique, called parametric t-SNE, that learns a parametric mapping between the high-dimensional data space and the low-dimensional latent space. Parametric t-SNE learns the parametric mapping in such a way that the local structure of the data is preserved as well as possible in the latent space. We evaluate the performance of parametric t-SNE in experiments on two datasets, in which we compare it to the performance of two other unsupervised parametric dimensionality reduction techniques. The results of experiments illustrate the strong performance of parametric t-SNE, in particular, in learning settings in which the dimensionality of the latent space is relatively low. ER -
APA
van der Maaten, L.. (2009). Learning a Parametric Embedding by Preserving Local Structure. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in PMLR 5:384-391

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