What is measure concentration and why is it central to the curse of dimensionality?

It states that in high dimensions, random variables become tightly concentrated around their expectation.

Measure concentration implies that functions over high-dimensional random vectors become almost constant. For example, Lipschitz functions over Gaussian spaces exhibit exponentially small deviation probabilities. This explains why distances, norms, and projections all concentrate, breaking discrimination power in ML models.