Abstract
Finding nearest neighbors in high-dimensional spaces is a very expensive task. Locality-sensitive hashing is a general dimension reduction technique that maps similar elements closely in the hash space, streamlining near neighbor lookup.
In this paper we propose a variable genome length biased random key genetic algorithm whose encoding facilitates the exploration of locality-sensitive hash functions that only use sparsely applied addition operations instead of the usual costly dense multiplications.
Experimental results show that the proposed method obtains highly efficient functions with a much higher mean average precision than standard methods using random projections, while also being much faster to compute.
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Acknowledgments
This paper was partially supported by the Sapientia Institute for Research Programs (KPI). The work of Sándor Miklós Szilágyi and László Szilágyi was additionally supported by the Hungarian Academy of Sciences through the János Bolyai Fellowship program.
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Iclanzan, D., Szilágyi, S.M., Szilágyi, L. (2018). Evolving Computationally Efficient Hashing for Similarity Search. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_49
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DOI: https://doi.org/10.1007/978-3-030-04179-3_49
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