|In Defense of Locality-Sensitive Hashing|
|Kun Ding; Chunlei Huo; Bin Fan; Shiming Xiang; Chunhong Pan
|Source Publication||IEEE Transactions on Neural Networks and Learning Systems
|Abstract||Hashing-based semantic similarity search is becoming increasingly important for building large-scale content-based retrieval system. The state-of-the-art supervised hashing techniques use flexible two-step strategy to learn hash functions. The first step learns binary codes for training data by solving binary optimization problems with millions of variables, thus usually requiring intensive computations. Despite simplicity and efficiency, locality-sensitive hashing (LSH) has never been recognized as a good way to generate such codes due to its poor performance in traditional approximate neighbor search. We claim in this paper that the true merit of LSH lies in transforming the semantic labels to obtain the binary codes, resulting in an effective and efficient two-step hashing framework. Specifically, we developed the locality-sensitive two-step hashing (LS-TSH) that generates the binary codes through LSH rather than any complex optimization technique. Theoretically, with proper assumption, LS-TSH is actually a useful LSH scheme, so that it preserves the label-based semantic similarity and possesses sublinear query complexity for hash lookup. Experimentally, LS-TSH could obtain comparable retrieval accuracy with state of the arts with two to three orders of magnitudes faster training speed.|
|Keyword||Locality-sensitive Hashing (Lsh)
Semantic Similarity Search
|Affiliation||National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences|
Kun Ding,Chunlei Huo,Bin Fan,et al. In Defense of Locality-Sensitive Hashing[J]. IEEE Transactions on Neural Networks and Learning Systems,2018,29(1):87-103.
Kun Ding,Chunlei Huo,Bin Fan,Shiming Xiang,&Chunhong Pan.(2018).In Defense of Locality-Sensitive Hashing.IEEE Transactions on Neural Networks and Learning Systems,29(1),87-103.
Kun Ding,et al."In Defense of Locality-Sensitive Hashing".IEEE Transactions on Neural Networks and Learning Systems 29.1(2018):87-103.
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.