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In multi-class classification using nearest neighbour, I believe that as the dimension of the space increases, we need exponentially more samples to keep the classification error under a certain threshold. Is there any theoretical support for this intuition? I am considering scenarios where the boundary is deterministic (not probabilistic) and the data points are uniformly distributed within a defined domain of real space.

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