I saw this neat comment in a paper I was recently reading. If you have all `i.i.d`

features and you want to estimate its dimension using Grassberger-Procaccia (which estimates dimension using a distance-based metric) or want to classify using a k-NN classifier, it is bad if the data points are mostly pairwise equidistant (for instance, a correlation integral plot will look like a step function and thus will be useless; a k-NN classifier will break because the test point ends up equidistant from all the existing points).

There is a trivial argument using the Hoeffding bound in Chris Burges’ paper that suggests that if the features are all `i.i.d`

, a majority of pairwise distances will end up clustered tightly around a mean which means that k-NN or Grassberger-Procaccia won’t work well. I am going to repeat this argument here so I can remember it for later:

Our vectors are of dimension $ d $ and the components are $ \pm1 $. Assuming all the components are $ iid $, the Hoeffding bound gives us:

$$ P(||| x_{1} - x_{2} ||^{2} – 2d| > d\epsilon) = P(| x_{1} \cdot x_{2} | > d\epsilon/2) \le 2exp(-\frac{d\epsilon^2}{8})$$

and this shows us that most pairwise distances will end up clustered very tightly around a mean and this means that a majority of pairs of points in the dataset will end up equidistant and thus a $ k-NN $ classifier will fail.

This also means that the correlation integral is a good way to determine if a k-NN classifier will work well. If the plot resembles a spike, the distance function needs to change.

The correlation-integral is an immensely powerful tool and here’s an implementation