[D] How can we bypass the Ugly Duckling Theorem in Unsupervised Representation Learning?

# Ugly Ducklings

I recently learned about the [Ugly Duckling Theorem](https://en.wikipedia.org/wiki/Ugly_duckling_theorem), which basically says that classification is impossible without some sort of bias.

More specifically, given a data set of n objects, there are 2^(n) possible groupings, and each object will be grouped with another object just as often as any other object, so some weighting on the possible attributes, some bias must be chosen so that classifying the objects make sense.

In the context of unsupervised learning, it seems …

bias data data set machinelearning objects representation representation learning set theorem unsupervised will