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T-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.

What is the difference between t-SNE and Stochastic Neighbor Embedding (SNE)?

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Actually, I find that because the t-Distribution is a long tail distribution, it prevents the crowding problem (which is one of the disadvantages of SNE).

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  • $\begingroup$ Interesting. Do you have any reference on this? $\endgroup$ – Firebug Aug 23 '17 at 19:30
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    $\begingroup$ @Firebug Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579-2605. $\endgroup$ – Iman Sep 12 '17 at 13:14
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The cluster structure produced by tSNE tend to be more separated, to have more stable shape; and be more repeatable.

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You can also watch this lecture from 17:48 to 20:12 to hear the reason with a great example from the author of t-SNE.

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