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Refining Codes for Locality Sensitive Hashing
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PRE
AI
DOI：10.1109/TKDE.2023.3297195.png)
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摘要
En 中文
Learning to hash is of particular interest in information retrieval for large-scale data due to its high efficiency and effectiveness. Most studies in hashing concentrate on constructing new hashing models, but rarely touch the correlation and redundancy between hash bits derived. In this article, we first introduce a general schema of hash bit reduction to derive compact and informative binary codes for hashing techniques. Further, we take locality sensitive hashing, one of the most widely-used hashing methods, as an example and propose a novel and two-stage binary code refinement method under the reduction schema. Specifically, the proposed method includes two stages, i.e., bit evaluation and bit refinement. The former stage aims to initially extract a small portion of informative hash bits in terms of their importance and quality evaluated by bit balance and similarity preservation. Then, the representation capabilities of the reduced hash bits are strengthened further by refining their binary values. The purpose of refinement is to lessen the correlations and redundancies between the reduced bits, making themselves more discriminative. The experimental results on three widely-used data collections confirm the effectiveness of the proposed bit reduction method and its superiority over the state-of-the-art hashing methods, as well as a bit selection method.
Keyword:
Binary codes
Hash functions
Redundancy
Correlation
Scalability
Visualization
Matrix decomposition
Hash learning
nearest neighbor search
information retrieval
locality sensitive hashing
期刊
IF:
10.4
论文数:
6.9K
被引数:
3.2W
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