I successfully applied t-SNE to the number handwriting dataset. n=3823 data points (i.e. handwritten numbers) in an D=64 dimensional space (i.e. 8x8 pixels). Worked great.

Now I would like to cluster n≈60 data points in an D≈3000 dimensional space. Even after many iterations, t-SNE fairs far worse than say PCA.

Is there an upper bound on the number of dimensions (relative to the number of data points) above which applying t-SNE is not adviced?

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    $\begingroup$ How do you know that t-SNE "fairs far worse" than say PCA? Also t-SNE has issues with clustering in general distill.pub/2016/misread-tsne $\endgroup$ – Brian Spiering Jan 8 '19 at 19:23
  • $\begingroup$ Brian has a good point, how you do say is doing worse? Do you have a quantitive measure in place or a 2d projection to to examine? Another suggestion is also to look at UMAP, it has shown to be faster and quite comparable in terms of identifying the manifold, see github.com/lmcinnes/umap! $\endgroup$ – TwinPenguins Jan 9 '19 at 6:52
  • $\begingroup$ We have three groups of patients which we expect to cluster accordingly. With PCA they do reasonably well, with t-SNE they do not. Brian Spiering and Majid Mortazavi, thanks for your quick replies and very interesting links. So, very high dimensionality is no inherent problem and I will give it another go and tune parameters. $\endgroup$ – lars20070 Jan 9 '19 at 8:24

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