Locality-Sensitive Hashing for Chi2 Distance

@article{Gorisse2012LocalitySensitiveHF,
  title={Locality-Sensitive Hashing for Chi2 Distance},
  author={David Gorisse and Matthieu Cord and Fr{\'e}d{\'e}ric Precioso},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2012},
  volume={34},
  pages={402-409}
}
In the past 10 years, new powerful algorithms based on efficient data structures have been proposed to solve the problem of Nearest Neighbors search (or Approximate Nearest Neighbors search). If the Euclidean Locality Sensitive Hashing algorithm, which provides approximate nearest neighbors in a euclidean space with sublinear complexity, is probably the most popular, the euclidean metric does not always provide as accurate and as relevant results when considering similarity measure as the Earth… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 19 references

Locality-sensitive hashing scheme based on p-stable distributions

Symposium on Computational Geometry • 2004
View 5 Excerpts
Highly Influenced

Product Quantization for Nearest Neighbor Search

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2011
View 2 Excerpts

Scalable active learning strategy for object category retrieval

2010 IEEE International Conference on Image Processing • 2010
View 1 Excerpt

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