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This package provides code to implement locality-sensitive hashing (LSH) in an optimum fashion. There are two pieces. A Python library that implements LSH and a Matlab routine that calculates the optimum parameters for LSH. The LSH implementation is based on a tutorial published by IEEE Malcolm Slaney, Michael Casey, "Locality-Sensitive Hashing for Finding Nearest Neighbors [Lecture Notes]," Signal Processing Magazine, IEEE , vol.25, no.2, pp.128-131, March 2008 doi: 10.1109/MSP.2007.914237 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4472264&isnumber=4472102 and also available at this URL http://www.slaney.org/malcolm/yahoo/Slaney2008-LSHTutorial.pdf The optimization algorithm is based on this article, which is currently under review. Send email to malcolm@ieee.org for a preprint Malcolm Slaney, Yury Lifshits, Junfeng He, "Optimal Locality-Sensitive Hashing," Submitted to Proceedings of the IEEE, Special Issue on Web-Scale Multimedia, Summer 2012. Send bug reports and/or comments to malcolm@ieee.org ################################################################ Copyright (c) 2011, Yahoo! Inc. All rights reserved. Redistribution and use of this software in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Yahoo! Inc. nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission of Yahoo! Inc. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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This package provides an efficient implementation of locality-sensitve hashing (LSH)
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