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  > So 'pointy' cubes reasons out the 'paradoxes'
  > without resorting to 'spikey' spheres.
See elsewhere why this comment misses the really important bits that relate to machine learning and high-dimensional visualizations.

In particular, this comment: http://news.ycombinator.com/item?id=3998259



Thanks for the point - but your argument that 'from a surface almost every step leads you out' doesn't seem to be true in particular.

Imagine a sphere in n-dimensions, S = {Sum(x_i^2)<1}. Top of the sphere is p=(1,0,0,...,0). Now for any random direction r, if you take a very small step from p towards r, you have exactly 50% chance to be inside the sphere.

To be mathematically precise, for any r (unit vector) chosen at random, probability that there is e>0 such that p + e*r is within S is 1/2.

So half the time the steps take you out, half the time it takes you in - considering the step is small enough compared to the radius.





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