  We propose a new embedding method for a single vector and for a pair of
vectors. This embedding method enables: a) efficient classification and
regression of functions of single vectors; b) efficient approximation of
distance functions; and c) non-Euclidean, semimetric learning. To the best of
our knowledge, this is the first work that enables learning any general,
non-Euclidean, semimetrics. That is, our method is a universal semimetric
learning and approximation method that can approximate any distance function
with as high accuracy as needed with or without semimetric constraints. The
project homepage including code is at: http://www.ariel.ac.il/sites/ofirpele/ID
