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Bit Reduction for Locality-Sensitive Hashing
IEEE Transactions on Neural Networks and Learning Systems
|April 10, 2023
Summary
Locality-sensitive hashing (LSH) generates many hash bits, hindering applications. This study introduces a novel schema and method to reduce hash bits, improving LSH efficiency and retrieval performance for large-scale data similarity search.
Area of Science:
- Computer Science
- Data Science
- Machine Learning
Background:
- Locality-sensitive hashing (LSH) is popular for large-scale similarity search.
- High numbers of hash bits in LSH present application challenges.
- Need for efficient binary code generation in hashing techniques.
Purpose of the Study:
- Introduce a novel hash bit reduction schema for hashing techniques.
- Present an effective bit reduction method for LSH under the proposed schema.
- Derive shorter binary codes without significantly deteriorating performance.
Main Methods:
- Generate hash bits using LSH.
- Utilize mutual information to measure hash bit correlation.
- Employ data labels to assess hash bit structural properties.
- Remove redundant hash bits based on correlation and structural analysis.
Main Results:
- Successfully reduced the number of hash bits effectively.
- Demonstrated boosted retrieval performance of LSH.
- Achieved encouraging and competitive performance on real-world datasets.
Conclusions:
- The proposed method enhances LSH practicality for real-world applications.
- Hash bit reduction is crucial for efficient large-scale similarity search.
- The approach offers a significant improvement over existing hashing algorithms.
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