E2LSH based on Gaussian kernel function optimization

Wentao Zhao

doi:10.1117/12.3012309

21 December 2023 E2LSH based on Gaussian kernel function optimization

Wentao Zhao

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297020 (2023) https://doi.org/10.1117/12.3012309
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

How to perform similarity search on high-dimensional data is a great challenge, especially for Euclidean distance, although the traditional E2LSH algorithm can cope with this problem well, the value of the parameter has a great influence on its effect. In this regard, a hash bucket parameter selection algorithm incorporating the Gaussian kernel function is proposed for its parameter selection problem, which improves the sensitivity to the data to obtain more accurate and efficient nearest neighbor search results in multi-distributed high dimensional data. Meanwhile, experimental comparisons with other similar algorithms are conducted on a common dataset, and their results show that the optimized E2LSH algorithm demonstrates improved performance in terms of speed and efficiency.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wentao Zhao "E2LSH based on Gaussian kernel function optimization", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297020 (21 December 2023); https://doi.org/10.1117/12.3012309

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