`
` Exhibit 47
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`
`
`Case 1:14-cv-02396-PGG-SN Document 241-22 Filed 11/12/20 Page 2 of 2
`LSH Algorithm and Implementation (E2LSH)
`
`Locality-Sensitive Hashing (LSH) is an algorithm for solving the (approximate/exact) Near Neighbor
`Search in high dimensional spaces. On this webpage, you will find pointers to the newest LSH algorithm in
`Euclidean (|_2) spaces, as well as the description of the EZ2LSH package, an implementation of this new
`algorithm for the Euclidean space.
`
`Algorithm description:
`o CACM survey of LSH (2008): "Near-Optimal Hashing Algorithms for Approximate Nearest
`Neighbor in High Dimensions” (by Alexandr Andoni and Piotr Indyk). Communications the
`(for free). local
`disciaimel).
`
`117-122.
`
`(see
`
`Hirectlyfrom
`
`1,
`
`ACM,vol.no.2008.pp.
`ome
`byAlex
`copy
`book
`
`CACMCACM
`Nearest
`
`Implementation of LSH: Currently, we only have an alpha-version available - the E2LSH package.
`The code is based on the algorithm described in the book chapter (2006) from above. Download tha
`code.
`You can also download the manual for the code to seeits functionality. The code has been developed
`Andonl in 2004-2005.
`
`Slides: Here are
`
`Foundations of Computer Science OCS
`slideg on the LSH algorithm from talk given by Piotr Indyk.
`© Earlier algorithm for Euclidean space (2004): a good introduction to LSH, and the description of
`affairs as of 2008,is in the following book chapter
`
`Scheme Based on p-Stable Disiributiong (by Alexandr Andoni, Mayur
`ocality-Sensitive
`Hashing
`Neighbor
`and Vahab
`appearing in the book
`Datar, Nicole Immorlica, Piotr ‘Indyk,
`Mirrokni),
`Jarrelland P.
`Learning and
`ion:
`Theory and Pra
`IindvK and
`Methods
`in
`(eds.), MIT Press, 2006.
`Shakhnarovich
`
`See also the
`
`introduction for a smooth introduction to NN problem and LSH.
`° Original LSH algorithm aw the best algorithm for the Hamming space remains the one
`described, e.g, in
`
`e
`
`This research is supported by NSF CAREER Grant #0133849 “Approximate Algorithms for High-dimensional
`Problems”.
`
`Geometric
`
`CONFIDENTIAL
`
`GOOG-NETWORK-00006362
`
`
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