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A Survey on Learning to Hash.

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    This survey categorizes learning to hash algorithms for efficient nearest neighbor search. Quantization methods offer superior search accuracy and reduced costs.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Data Mining

    Background:

    • Nearest neighbor search is crucial for data retrieval.
    • Learning to hash (L2H) is a key technique for efficient nearest neighbor search.
    • Recent advancements have led to diverse L2H algorithms.

    Purpose of the Study:

    • To provide a comprehensive survey of learning to hash algorithms.
    • To categorize L2H methods based on similarity preservation.
    • To analyze the performance and relations of different L2H approaches.

    Main Methods:

    • Categorization of L2H algorithms into pairwise, multiwise, implicit similarity preserving, and quantization.
    • Discussion of the relationships between these categories.
    • Presentation of evaluation protocols and performance analysis.

    Main Results:

    • Quantization is distinct from pairwise similarity preservation due to differing objective functions, yet derivable from it.
    • Quantization algorithms demonstrate superior performance in search accuracy, time, and space costs.
    • A clear categorization and comparison of various L2H techniques are established.

    Conclusions:

    • Learning to hash is a vital area for efficient nearest neighbor search.
    • Quantization-based L2H methods are highly effective and efficient.
    • Emerging topics in L2H warrant further research and development.