Randomized Sampling-Based Fly Local Sensitive Hashing
Kuan Xu, Yu Qiao · 2018
Fly Local Sensitive Hashing (FLSH) is a biomimetic data-independent hashing method inspired by the mechanism of odor processing system in drosophila. In this paper, we propose a novel Randomized Sampling-based Fly Local Sensitive Hashing (rs-FLSH) to model the randomness occurred during the establishment of synapses between neurons. Significant performance improvement can be achieved by applying a novel randomized sampling scheme in rs-FLSH, in which the sample rate is modeled by a Gaussian random variable rather than a fixed value in FLSH. Experimental results on benchmark dataset show that our proposed method outperforms FLSH, and achieves considerable performance comparing to several data-dependent hashing methods.