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Super-Bit Locality-Sensitive Hashing
Jianqiu Ji · Jianmin Li · shuicheng yan · Bo Zhang · Qi Tian

Wed Dec 05 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor
Sign-random-projection locality-sensitive hashing (SRP-LSH) is a probabilistic dimension reduction method which provides an unbiased estimate of angular similarity, yet suffers from the large variance of its estimation. In this work, we propose the Super-Bit locality-sensitive hashing (SBLSH). It is easy to implement, which orthogonalizes the random projection vectors in batches, and it is theoretically guaranteed that SBLSH also provides an unbiased estimate of angular similarity, yet with a smaller variance when the angle to estimate is within $(0,\pi/2]$. The extensive experiments on real data well validate that given the same length of binary code, SBLSH may achieve significant mean squared error reduction in estimating pairwise angular similarity. Moreover, SBLSH shows the superiority over SRP-LSH in approximate nearest neighbor (ANN) retrieval experiments.

Author Information

Jianqiu Ji (Tsinghua University)
Jianmin Li (Tsinghua University)
shuicheng yan (National University of Singapore)
Bo Zhang (Fair Isaac Corp.)
Qi Tian (University of Texas at San Antonio)

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