0) IntroductionDistance metrics, like Euclidean, Manhattan, Minkowsky and so on, suffer a lot when we increase the number of dimensions (features) of our data. Its like they dilute and loose their meaning. The become unreliable. This is not very easy to understand, and it comes into conflict with the general understanding that more features equals to better Machine Learning models, which is far fr
I read that 'Euclidean distance is not a good distance in high dimensions'. I guess this statement has something to do with the curse of dimensionality, but what exactly? Besides, what is 'high dimensions'? I have been applying hierarchical clustering using Euclidean distance with 100 features. Up to how many features is it 'safe' to use this metric?
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