What makes t-sne supervised?
Wikipedia classifies the t-sne algorithm as a supervised method. I read that supervised methods involve training, with an input and a desired result.
I was thinking, the goal of t-sne is to minimize the Kullback–Leibler divergence. Does minimizing this divergence count as a "desired result", which makes it supervised?
Wikipedia does not classify t-sne as supervised learning but as dimensionality reduction (at the moment I am writing the answer). And, as far as I know, it is not a supervised method at all.
Its purpose is to ease the data visualization, reduce dimentionality and can also be used as a clustering technique (unsupervised classification).