Should data be centered+scaled before applying t-SNE?

Question

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.

score 29 · Accepted Answer · answered Jun 09 '16 at 18:52

29

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.

answered Jun 09 '16 at 18:52

jon_simon

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By "rescaling" do you mean Min-Max scaling or Standardization? – helperFunction Dec 06 '20 at 07:42
Standardization – jon_simon Dec 06 '20 at 09:22

Should data be centered+scaled before applying t-SNE?

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