Some of my data's features have large values, while other features have much smaller values.

Is it necessary to center+scale data before applying t-SNE to prevent bias towards the larger values?

I use Python's sklearn.manifold.TSNE implementation with the default euclidean distance metric.


1 Answer 1


Centering shouldn't matter since the algorithm only operates on distances between points, however rescaling is necessary if you want the different dimensions to be treated with equal importance, since the 2-norm will be more heavily influenced by dimensions with large variance.

  • $\begingroup$ By "rescaling" do you mean Min-Max scaling or Standardization? $\endgroup$ Dec 6, 2020 at 7:42
  • 1
    $\begingroup$ Standardization $\endgroup$
    – jon_simon
    Dec 6, 2020 at 9:22

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