| Summary: | Locality-sensitive hashing (LSH) has attracted extensive research efforts for approximate nearest neighbors (NN) search. However, most of these LSH-based index structures fail to take data distribution into account. They perform well in a uniform data distribution setting but exhibit unstable performance when the data are skewed. As known, most real life data are skewed, which makes LSH suffer. In this paper, we observe that the skewness of hash values resulted from skewed data is a potential reason for performance degradation. To address this problem, we propose to rebuild LSH indices by exploring the density of hash values. The hash values in dense/sparse ranges are carefully reorganized using a multi-layered structure, so that more efforts are put into indexing the dense hash values. We further discuss the benefit in distributed computing. Extensive experiments are conducted to show the effectiveness and efficiency of the reconstructed LSH indices.
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