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Locality Sensitive Hashing Using GMM

  • Fabian SchmiederEmail author
  • Bin Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

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.

Keywords

Hash Function Gaussian Mixture Model Query Time Section Length Neighbor Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.ISSUniversity of StuttgartStuttgartGermany

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