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.
Cite
CITATION STYLE
APA
Schmieder, F., & Yang, B. (2014). Locality sensitive hashing using GMM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8753, pp. 569–581). Springer Verlag. https://doi.org/10.1007/978-3-319-11752-2_47
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