We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each
datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic
Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces
significantly better visualizations by reducing the tendency to crowd points together in the center
of the map. t-SNE is better than existing techniques at creating a single map that reveals structure
at many different scales. This is particularly important for high-dimensional data that lie on several
different, but related, low-dimensional manifolds, such as images of objects from multiple classes
seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how
t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the
data to influence the way in which a subset of the data is displayed. We illustrate the performance of
t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization
techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualiza-
tions produced by t-SNE are significantly better than those produced by the other techniques on
almost all of the data sets.
%0 Journal Article
%1 vandermaaten2008visualizing
%A Van der Maaten, Laurens
%A Hinton, Geoffrey
%D 2008
%J Journal of Machine Learning Research
%K t-SNE
%P 2579--2605
%T Visualizing data using t-SNE
%U http://www.jmlr.org/papers/v9/vandermaaten08a.html
%V 9
%X We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each
datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic
Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces
significantly better visualizations by reducing the tendency to crowd points together in the center
of the map. t-SNE is better than existing techniques at creating a single map that reveals structure
at many different scales. This is particularly important for high-dimensional data that lie on several
different, but related, low-dimensional manifolds, such as images of objects from multiple classes
seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how
t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the
data to influence the way in which a subset of the data is displayed. We illustrate the performance of
t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization
techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualiza-
tions produced by t-SNE are significantly better than those produced by the other techniques on
almost all of the data sets.
@article{vandermaaten2008visualizing,
abstract = {We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each
datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic
Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces
significantly better visualizations by reducing the tendency to crowd points together in the center
of the map. t-SNE is better than existing techniques at creating a single map that reveals structure
at many different scales. This is particularly important for high-dimensional data that lie on several
different, but related, low-dimensional manifolds, such as images of objects from multiple classes
seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how
t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the
data to influence the way in which a subset of the data is displayed. We illustrate the performance of
t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization
techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualiza-
tions produced by t-SNE are significantly better than those produced by the other techniques on
almost all of the data sets.},
added-at = {2021-09-08T04:39:59.000+0200},
author = {Van der Maaten, Laurens and Hinton, Geoffrey},
biburl = {https://www.bibsonomy.org/bibtex/2512d1f975b474127c32b064055748a83/andolab},
interhash = {370ba8b9e1909b61880a6f47c93bcd49},
intrahash = {512d1f975b474127c32b064055748a83},
journal = {Journal of Machine Learning Research},
keywords = {t-SNE},
month = nov,
pages = {2579--2605},
timestamp = {2023-01-31T20:34:07.000+0100},
title = {Visualizing data using t-SNE},
url = {http://www.jmlr.org/papers/v9/vandermaaten08a.html},
volume = 9,
year = 2008
}