A General and Efficient Querying Method for Learning to Hash
Refereed conference paper presented and published in conference proceedings

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AbstractAs an effective solution to the approximate nearest neighbors (ANN) search problem, learning to hash (L2H) is able to learn similarity-preserving hash functions tailored for a given dataset. However, existing L2H research mainly focuses on improving query performance by learning good hash functions, while Hamming ranking (HR) is used as the default querying method. We show by analysis and experiments that Hamming distance, the similarity indicator used in HR, is too coarse-grained and thus limits the performance of query processing. We propose a new fine-grained similarity indicator, quantization distance (QD), which provides more information about the similarity between a query and the items in a bucket. We then develop two efficient querying methods based on QD, which achieve significantly better query performance than HR. Our methods are general and can work with various L2H algorithms. Our experiments demonstrate that a simple and elegant querying method can produce performance gain equivalent to advanced and complicated learning algorithms.
All Author(s) ListJinfeng Li, Xiao Yan, Jian Zhang, An Xu, James Cheng, Jie Liu, Kelvin K. W. Ng, Ti-chung Cheng
Name of Conference44th ACM SIGMOD International Conference on Management of Data, SIGMOD 2018
Start Date of Conference10/06/2018
End Date of Conference15/06/2018
Place of ConferenceHouston, TX
Country/Region of ConferenceUnited States of America
LanguagesEnglish-United States

Last updated on 2021-12-06 at 02:17