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)


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.


Hash Function Gaussian Mixture Model Query Time Section Length Neighbor Problem 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.ISSUniversity of StuttgartStuttgartGermany

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