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Results 1 - 10
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126
Locality-sensitive hashing scheme based on p-stable distributions
- In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry
, 2004
"... inÇÐÓ�Ò We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate ..."
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Cited by 521 (8 self)
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inÇÐÓ�Ò We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate
Time-Space Trade-Offs for Longest Common Extensions
- In Proc. 23rd CPM, LNCS
, 2012
"... We revisit the longest common extension (LCE) problem, that is, preprocess a string T into a compact data structure that supports fast LCE queries. An LCE query takes a pair (i, j) of indices in T and returns the length of the longest common prefix of the suffixes of T starting at positions i and j. ..."
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Cited by 4 (3 self)
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. We study the time-space trade-offs for the problem, that is, the space used for the data structure vs. the worst-case time for answering an LCE query. Let n be the length of T. Given a parameter τ, 1 ≤ τ ≤ n, we show how to achieve either O(n/ τ) space and O(τ) query time, or O(n/τ) space and O(τ log
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in HighDimensions*
"... Abstract We present an algorithm for the c-approximate near-est neighbor problem in a d-dimensional Euclidean space,achieving query time of O(dn1/c 2+o(1)) and space O(dn + n1+1/c 2+o(1)). This almost matches the lower bound for hashing-based algorithm recently obtained in [27]. We alsoobtain a spac ..."
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Abstract We present an algorithm for the c-approximate near-est neighbor problem in a d-dimensional Euclidean space,achieving query time of O(dn1/c 2+o(1)) and space O(dn + n1+1/c 2+o(1)). This almost matches the lower bound for hashing-based algorithm recently obtained in [27]. We alsoobtain a
Y.: Locally optimized product quantization for approximate nearest neighbor search
, 2014
"... We present a simple vector quantizer that combines low distortion with fast search and apply it to approximate near-est neighbor (ANN) search in high dimensional spaces. Leveraging the very same data structure that is used to pro-vide non-exhaustive search, i.e., inverted lists or a multi-index, the ..."
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Cited by 5 (0 self)
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We present a simple vector quantizer that combines low distortion with fast search and apply it to approximate near-est neighbor (ANN) search in high dimensional spaces. Leveraging the very same data structure that is used to pro-vide non-exhaustive search, i.e., inverted lists or a multi
Optimizing Affinity-Based Binary Hashing Using Auxiliary Coordinates
"... Abstract In supervised binary hashing, one wants to learn a function that maps a highdimensional feature vector to a vector of binary codes, for application to fast image retrieval. This typically results in a difficult optimization problem, nonconvex and nonsmooth, because of the discrete variable ..."
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Cited by 1 (1 self)
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O(N D) and space O(N D) (to store the image dataset). In practice, this is approximated, and a successful way to do this is binary hashing The disadvantage is that the results are inexact, since the neighbors in the binary space will not be identical to the neighbors in the original space. However
Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles
, 2015
"... Popular outlier detection methods require the pairwise comparison of objects to compute the nearest neighbors. This inherently quadratic problem is not scalable to large data sets, making multidimensional outlier detection for big data still an open challenge. Existing approximate neighbor search me ..."
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Cited by 1 (0 self)
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methods are designed to preserve distances as well as possible. In this article, we present a highly scalable approach to compute the nearest neighbors of objects that instead focuses on preserving neighborhoods well using an ensemble of space-filling curves. We show that the method has near
Fast Subspace Search via Grassmannian Based Hashing
"... The problem of efficiently deciding which of a database of models is most similar to a given input query arises throughout modern computer vision. Motivated by appli-cations in recognition, image retrieval and optimization, there has been significant recent interest in the variant of this problem in ..."
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Cited by 3 (1 self)
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in which the database models are linear sub-spaces and the input is either a point or a subspace. Cur-rent approaches to this problem have poor scaling in high dimensions, and may not guarantee sublinear query com-plexity. We present a new approach to approximate near-est subspace search, based on a simple
Mining Mass Spectra: Metric Embeddings and Fast Near Neighbor Search
, 2008
"... Mining large-scale high-throughput tandem mass spectrometry data sets is a very important problem in mass spectrometry based protein identification. One of the fundamental problems in large scale mining of spectra is to design appropriate metrics and algorithms to avoid all-pair-wise comparisons of ..."
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of spectra. In this paper, we present a general framework based on vector spaces to avoid pair-wise comparisons. We first robustly embed spectra in a high dimensional space in a novel fashion and then apply fast approximate near neighbor algorithms for tasks such as constructing filters for database search
Approximate Caches for Packet Classification
- In IEEE INFOCOM
, 2004
"... Many network devices such as routers and firewalls employ caches to take advantage of temporal locality of packet headers in order to speed up packet processing decisions. Traditionally, cache designs trade off time and space with the goal of balancing the overall cost and performance of the device. ..."
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Cited by 36 (1 self)
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Many network devices such as routers and firewalls employ caches to take advantage of temporal locality of packet headers in order to speed up packet processing decisions. Traditionally, cache designs trade off time and space with the goal of balancing the overall cost and performance of the device
Better ε-Dependencies for Offline Approximate Nearest Neighbor Search, Euclidean Minimum Spanning Trees, and ε-Kernels
"... Recently, Arya, da Fonseca, and Mount [STOC 2011, SODA 2012] made notable progress in improving the ε-dependencies in the space/query-time tradeoffs for (1 + ε)-factor approximate nearest neighbor search in fixed-dimensional Euclidean spaces. However, ε-dependencies in the preprocessing time were no ..."
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Recently, Arya, da Fonseca, and Mount [STOC 2011, SODA 2012] made notable progress in improving the ε-dependencies in the space/query-time tradeoffs for (1 + ε)-factor approximate nearest neighbor search in fixed-dimensional Euclidean spaces. However, ε-dependencies in the preprocessing time were
Results 1 - 10
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126
