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Locality-preserving hashing in multidimensional spaces
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Approximate nearest neighbors: towards removing the curse of dimensionality
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VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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WWW '05 Proceedings of the 14th international conference on World Wide Web
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SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
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SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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APPROX '08 / RANDOM '08 Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques
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SIAM Journal on Computing
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ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
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IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Streaming similarity search over one billion tweets using parallel locality-sensitive hashing
Proceedings of the VLDB Endowment
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Locality-sensitive hashing (LSH) is a basic primitive in several large-scale data processing applications, including nearest-neighbor search, de-duplication, clustering, etc. In this paper we propose a new and simple method to speed up the widely-used Euclidean realization of LSH. At the heart of our method is a fast way to estimate the Euclidean distance between two d-dimensional vectors; this is achieved by the use of randomized Hadamard transforms in a non-linear setting. This decreases the running time of a (k, L)-parameterized LSH from O(dkL) to O(dlog d + kL). Our experiments show that using the new LSH in nearest-neighbor applications can improve their running times by significant amounts. To the best of our knowledge, this is the first running time improvement to LSH that is both provable and practical.