  The era of Big Data has spawned unprecedented interests in developing hashing
algorithms for efficient storage and fast nearest neighbor search. Most
existing work learn hash functions that are numeric quantizations of feature
values in projected feature space. In this work, we propose a novel hash
learning framework that encodes feature's rank orders instead of numeric values
in a number of optimal low-dimensional ranking subspaces. We formulate the
ranking subspace learning problem as the optimization of a piece-wise linear
convex-concave function and present two versions of our algorithm: one with
independent optimization of each hash bit and the other exploiting a sequential
learning framework. Our work is a generalization of the Winner-Take-All (WTA)
hash family and naturally enjoys all the numeric stability benefits of rank
correlation measures while being optimized to achieve high precision at very
short code length. We compare with several state-of-the-art hashing algorithms
in both supervised and unsupervised domain, showing superior performance in a
number of data sets.
