  Hyperplane hashing aims at rapidly searching nearest points to a hyperplane,
and has shown practical impact in scaling up active learning with SVMs.
Unfortunately, the existing randomized methods need long hash codes to achieve
reasonable search accuracy and thus suffer from reduced search speed and large
memory overhead. To this end, this paper proposes a novel hyperplane hashing
technique which yields compact hash codes. The key idea is the bilinear form of
the proposed hash functions, which leads to higher collision probability than
the existing hyperplane hash functions when using random projections. To
further increase the performance, we propose a learning based framework in
which the bilinear functions are directly learned from the data. This results
in short yet discriminative codes, and also boosts the search performance over
the random projection based solutions. Large-scale active learning experiments
carried out on two datasets with up to one million samples demonstrate the
overall superiority of the proposed approach.
