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Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
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Communications of the ACM archive
Volume 51 ,  Issue 1  (January 2008) table of contents
50th anniversary issue: 1958 - 2008
SPECIAL ISSUE: Breakthrough research: a preview of things to come table of contents
Pages 117-122  
Year of Publication: 2008
ISSN:0001-0782
Authors
Alexandr Andoni
Piotr Indyk  Massachusetts Institute of Technology, Cambridge, MA
Publisher
ACM  New York, NY, USA
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APPENDICES and SUPPLEMENTS
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ABSTRACT

In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query. The problem is of significant interest in a wide variety of areas.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Andoni, A. and Indyk, P. 2004. E2lsh: Exact Euclidean localitysensitive hashing. http://web.mit.edu/andoni/www/LSH/.
 
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Haveliwala, T., Gionis, A., and Indyk, P. 2000. Scalable techniques for clustering the web. WebDB Workshop.
 
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Indyk, P. 2003. Nearest neighbors in high-dimensional spaces. In Handbook of Discrete and Computational Geometry. CRC Press.
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Terasawa, T. and Tanaka, Y. 2007. Spherical lsh for approximate nearest neighbor search on unit hypersphere. In Proceedings of the Workshop on Algorithms and Data Structures.


Collaborative Colleagues:
Alexandr Andoni: colleagues
Piotr Indyk: colleagues