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T-distributed stochastic neighbor embedding (t-SNE) is a nonlinear dimensionality reduction algorithm introduced by van der Maaten and Hinton in 2008.

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What classification algorithm should one use after seeing that t-SNE separates classes well?

Let's assume we have a classification problem and at first we want to get some insight from the data and we do t-SNE. The result of t-SNE separates classes very well. This implies that it is possible …
Tomek Tarczynski's user avatar