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Reading about t-SNE, and looking at the pretty plots, it seems to be very good at separating things that we "expect" to be separate in low dimensions. Why wouldn't we use this to do dimensionality reduction before using some sort of classification algorithm (something data-hungry like a DNN for example)?

EDIT: to rephrase and generalize slightly further, since t-SNE preserves separatbility so well when it is nicely tuned, why not go for t-SNE in 2 or 3-D and then a nonlinear classifier, instead of a standard $k$-dimension reduction like PCA or ICA?

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  • $\begingroup$ Possible duplicate: stats.stackexchange.com/questions/263539/… $\endgroup$ – generic_user Oct 26 '17 at 13:10
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    $\begingroup$ I think this is a little different, just because KMM for example is a generative approach, and as the answer well points out much information is lost in terms of density. But if separation is preserved, then why not run a complicated discriminative approach? $\endgroup$ – bibliolytic Oct 26 '17 at 13:24
  • $\begingroup$ IDK, is separation truly preserved? $\endgroup$ – generic_user Oct 26 '17 at 13:44
  • $\begingroup$ Heuristically speaking I'd say yes, to look at van Maaten's papers $\endgroup$ – bibliolytic Nov 1 '17 at 16:01

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