Quote:
Originally Posted by jlaurentum
Hello:
In slide 9 of lecture 5 (minute 33:03), the Professor gives an example of 3 colinear points for which there can be no possible hypothesis. Still, "it doesn't bother us because we want the maximum bound of possible dichotomies", so k=3 is not considered as a breakpoint. My question is:
In a ddimensional perceptron, it appears we would not consider a set of points lying in a (d1)dimensional hyperplane as candidates for giving an "impossible" dichotomy. Why? Is it because the probability of picking such a set of points from the input space that all lie in a (d1) dimensional space is zero? (As in the case of picking 3 collinear points in a plane).

It's worth observing that the set
of
dimensional perceptrons, restricted to a
dimensional subspace, is simply
, the set of
dimensional perceptrons on that subspace. hence, the capabilities of
restricted to the subspace is the same as that of
.
It turns out that the power of the hypothesis set comprising perceptrons increases as the dimension of their domain increases. The three points are a good example. If colinear, they cannot be shattered, regardless of what dimension space they are in. If not colinear, they can always be shattered: this requires the domain to be at least
dimensional.