Journal ArticleDOI
Non-Expansive Hashing
Nathan Linial,Ori Sasson +1 more
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TLDR
A non-expansive hashing scheme wherein any set of size from a large universe may be stored in a memory of size (any, and ), and where retrieval takes operations.Abstract:
hashing scheme, similar inputs are stored in memory locations which are close. We develop a non-expansive hashing scheme wherein any set of size from a large universe may be stored in a memory of size (any , and ), and where retrieval takes operations. We explain how to use non-expansive hashing schemes for efficient storage and retrieval of noisy data. A dynamic version of this hashing scheme is presented as well.read more
Citations
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Proceedings ArticleDOI
Similarity estimation techniques from rounding algorithms
TL;DR: It is shown that rounding algorithms for LPs and SDPs used in the context of approximation algorithms can be viewed as locality sensitive hashing schemes for several interesting collections of objects.
Proceedings ArticleDOI
Efficient search for approximate nearest neighbor in high dimensional spaces
TL;DR: Significantly improving and extending recent results of Kleinberg, data structures whose size is polynomial in the size of the database and search algorithms that run in time nearly linear or nearly quadratic in the dimension are constructed.
Proceedings ArticleDOI
Uniform hashing in constant time and linear space
Anna Östlin,Rasmus Pagh +1 more
TL;DR: This paper presents an almost ideal solution to this problem: a hash function that, on any set of n inputs, behaves like a truly random function with high probability, can be evaluated in constant time on a RAM, and can be stored in O(n) words, which is optimal.
Journal ArticleDOI
Simulating Uniform Hashing in Constant Time and Optimal Space
Anna Östlin,Rasmus Pagh +1 more
TL;DR: In this paper it is shown how to implement hash functions that can be evaluated on a RAM in constant time, and behave like truly random functions on any set of n inputs, with high probability.
Proceedings Article
Fast local searches and updates in bounded universes.
TL;DR: In this paper, it was shown how to perform predecessor searches in O( log log Δ ) expected time, where Δ is the difference between the element being searched for and its predecessor in the structure.
References
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Journal ArticleDOI
Universal classes of hash functions
TL;DR: An input independent average linear time algorithm for storage and retrieval on keys that makes a random choice of hash function from a suitable class of hash functions.
Book
Sparse Distributed Memory
TL;DR: Pentti Kanerva's Sparse Distributed Memory presents a mathematically elegant theory of human long term memory that resembles the cortex of the cerebellum, and provides an overall perspective on neural systems.
Journal ArticleDOI
Storing a Sparse Table with 0(1) Worst Case Access Time
TL;DR: A data structure for representing a set of n items from a universe of m items, which uses space n+o(n) and accommodates membership queries in constant time and is easy to implement.
Proceedings ArticleDOI
Dynamic perfect hashing: upper and lower bounds
Martin Dietzfelbinger,Anna R. Karlin,Kurt Mehlhorn,F.M. auf der Heide,Hans Rohnert,Robert E. Tarjan +5 more
TL;DR: In this article, a randomized algorithm with O(1) worst-case time for lookup and O( 1) amortized expected time for insertion and deletion was given for the dictionary problem.
Proceedings ArticleDOI
Multi-index hashing for information retrieval
TL;DR: A technique for building hash indices for a large dictionary of strings that permits robust retrieval of strings from the dictionary even when the query pattern has a significant number of errors.