Abstract
We propose a new approach for locality sensitive hashes (LSH) solving the approximate nearest neighbor problem. A well known LSH family uses linear projections to place the samples of a dataset into different buckets. We extend this idea and, instead of using equally spaced buckets, use a Gaussian mixture model to build a data dependent mapping.
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Schmieder, F., Yang, B. (2014). Locality Sensitive Hashing Using GMM. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_47
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DOI: https://doi.org/10.1007/978-3-319-11752-2_47
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