Vapnik-Chervonenkis Dimension of Axis-Parallel Cuts

Abstract: The Vapnik-Chervonenkis (VC) dimension of the set of half-spaces of R^d with frontiers parallel to the axes is computed exactly. It is shown that it is much smaller than the intuitive value of d. A good approximation based on the Stirling's formula proves that it is more likely of the order log_2(d). This result may be used to evaluate the performance of classifiers or regressors based on dyadic partitioning of R^d for instance. Algorithms using axis-parallel cuts to partition R^d are often used to reduce the computational time of such estimators when d is large.