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107
Cover trees for nearest neighbor
 In Proceedings of the 23rd international conference on Machine learning
, 2006
"... ABSTRACT. We present a tree data structure for fast nearest neighbor operations in generalpoint metric spaces. The data structure requires space regardless of the metric’s structure. If the point set has an expansion constant � in the sense of Karger and Ruhl [KR02], the data structure can be const ..."
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Cited by 218 (0 self)
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ABSTRACT. We present a tree data structure for fast nearest neighbor operations in generalpoint metric spaces. The data structure requires space regardless of the metric’s structure. If the point set has an expansion constant � in the sense of Karger and Ruhl [KR02], the data structure can be constructed in � time. Nearest neighbor queries obeying the expansion bound require � time. In addition, the nearest neighbor of points can be queried in time. We experimentally test the algorithm showing speedups over the brute force search varying between 1 and 2000 on natural machine learning datasets. 1.
Fast construction of nets in lowdimensional metrics and their applications
 SIAM Journal on Computing
, 2006
"... We present a near linear time algorithm for constructing hierarchical nets in finite metric spaces with constant doubling dimension. This datastructure is then applied to obtain improved algorithms for the following problems: approximate nearest neighbor search, wellseparated pair decomposition, s ..."
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Cited by 130 (14 self)
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We present a near linear time algorithm for constructing hierarchical nets in finite metric spaces with constant doubling dimension. This datastructure is then applied to obtain improved algorithms for the following problems: approximate nearest neighbor search, wellseparated pair decomposition, spanner construction, compact representation scheme, doubling measure, and computation of the (approximate) Lipschitz constant of a function. In all cases, the running (preprocessing) time is near linear and the space being used is linear. 1
An investigation of practical approximate nearest neighbor algorithms
, 2004
"... This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate neares ..."
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Cited by 115 (4 self)
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This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate nearest neighbor approach called Locality Sensitive Hashing (LSH). In this paper we ask the question: can earlier spatial data structure approaches to exact nearest neighbor, such as metric trees, be altered to provide approximate answers to proximity queries and if so, how? We introduce a new kind of metric tree that allows overlap: certain datapoints may appear in both the children of a parent. We also introduce new approximate kNN search algorithms on this structure. We show why these structures should be able to exploit the same randomprojectionbased approximations that LSH enjoys, but with a simpler algorithm and perhaps with greater efficiency. We then provide a detailed empirical evaluation on five large, high dimensional datasets which show up to 31fold accelerations over LSH. This result holds true throughout the spectrum of approximation levels.
LocalitySensitive Binary Codes from ShiftInvariant Kernels
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
, 2009
"... This paper addresses the problem of designing binary codes for highdimensional data such that vectors that are similar in the original space map to similar binary strings. We introduce a simple distributionfree encoding scheme based on random projections, such that the expected Hamming distance be ..."
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Cited by 81 (1 self)
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This paper addresses the problem of designing binary codes for highdimensional data such that vectors that are similar in the original space map to similar binary strings. We introduce a simple distributionfree encoding scheme based on random projections, such that the expected Hamming distance between the binary codes of two vectors is related to the value of a shiftinvariant kernel (e.g., a Gaussian kernel) between the vectors. We present a full theoretical analysis of the convergence properties of the proposed scheme, and report favorable experimental performance as compared to a recent stateoftheart method, spectral hashing.
Nearest Neighbor Preserving Embeddings
"... In this paper we introduce the notion of nearest neighbor preserving embeddings. These are randomized embeddings between two metric spaces which preserve the (approximate) nearest neighbors. We give two examples of such embeddings, for Euclidean metrics with low “intrinsic” dimension. Combining the ..."
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Cited by 48 (2 self)
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In this paper we introduce the notion of nearest neighbor preserving embeddings. These are randomized embeddings between two metric spaces which preserve the (approximate) nearest neighbors. We give two examples of such embeddings, for Euclidean metrics with low “intrinsic” dimension. Combining the embeddings with known data structures yields the best known approximate nearest neighbor data structures for such metrics.
Wireless scheduling with power control
 In Proc. 17th European Symposium on Algorithms (ESA
, 2009
"... We consider the scheduling of arbitrary wireless links in the physical model of interference to minimize the time for satisfying all requests. We study here the combined problem of scheduling and power control, where we seek both an assignment of power settings and a partition of the links so that e ..."
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Cited by 43 (6 self)
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We consider the scheduling of arbitrary wireless links in the physical model of interference to minimize the time for satisfying all requests. We study here the combined problem of scheduling and power control, where we seek both an assignment of power settings and a partition of the links so that each set satisfies the signaltointerferenceplusnoise (SINR) constraints. We give an algorithm that attains an approximation ratio of O(log n · log log Λ), where Λ is the ratio between the longest and the shortest linklength. Under the natural assumption that lengths are represented in binary, this gives the first polylog(n)approximation. The algorithm has the desirable property of using an oblivious power assignment, where the power assigned to a sender depends only on the length of the link. We show this dependence on Λ to be unavoidable, giving a construction for which any oblivious power assignment results in a Ω(log log Λ)approximation. We also give a simple online algorithm that yields a O(log Λ)approximation, by a reduction to the coloring of unitdisc graphs. In addition, we obtain improved approximation for a bidirectional variant of the scheduling problem, give partial answers to questions about the utility of graphs for modeling physical interference, and generalize the setting from the standard 2dimensional Euclidean plane to doubling metrics. 1
Clustering Billions of Images with Large Scale Nearest Neighbor Search
 in IEEE Workshop on Applications of Computer Vision
, 2007
"... The proliferation of the web and digital photography have made large scale image collections containing billions of images a reality. Image collections on this scale make performing even the most common and simple computer vision, image processing, and machine learning tasks nontrivial. An example ..."
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Cited by 29 (1 self)
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The proliferation of the web and digital photography have made large scale image collections containing billions of images a reality. Image collections on this scale make performing even the most common and simple computer vision, image processing, and machine learning tasks nontrivial. An example is nearest neighbor search, which not only serves as a fundamental subproblem in many more sophisticated algorithms, but also has direct applications, such as image retrieval and image clustering. In this paper, we address the nearest neighbor problem as the first step towards scalable image processing. We describe a scalable version of an approximate nearest neighbor search algorithm and discuss how it can be used to find near duplicates among over a billion images. 1.
Fast construction of kNearest Neighbor Graphs for Point Clouds
"... Abstract—We present a parallel algorithm for knearest neighbor graph construction that uses Morton ordering. Experiments show that our approach has the following advantages over existing methods: (1) Faster construction of knearest neighbor graphs in practice on multicore machines. (2) Less space ..."
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Cited by 28 (1 self)
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Abstract—We present a parallel algorithm for knearest neighbor graph construction that uses Morton ordering. Experiments show that our approach has the following advantages over existing methods: (1) Faster construction of knearest neighbor graphs in practice on multicore machines. (2) Less space usage. (3) Better cache efficiency. (4) Ability to handle large data sets. (5) Ease of parallelization and implementation. If the point set has a bounded expansion constant, our algorithm requires one comparison based parallel sort of points according to Morton order plus near linear additional steps to output the knearest neighbor graph. Index Terms—Nearest neighbor searching, point based graphics, knearest neighbor graphics, Morton Ordering, parallel algorithms. 1
Ramsey partitions and proximity data structures
 J. European Math. Soc 9
"... This paper addresses two problems lying at the intersection of geometric analysis and theoretical computer science: The nonlinear isomorphic Dvoretzky theorem and the design of good approximate distance oracles for large distortion. We introduce the notion of Ramsey partitions of a finite metric sp ..."
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Cited by 26 (3 self)
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This paper addresses two problems lying at the intersection of geometric analysis and theoretical computer science: The nonlinear isomorphic Dvoretzky theorem and the design of good approximate distance oracles for large distortion. We introduce the notion of Ramsey partitions of a finite metric space, and show that the existence of good Ramsey partitions implies a solution to the metric Ramsey problem for large distortion (a.k.a. the nonlinear version of the isomorphic Dvoretzky theorem, as introduced by Bourgain, Figiel, and Milman in [8]). We then proceed to construct optimal Ramsey partitions, and use them to show that for everyε∈(0, 1), any npoint metric space has a subset of size n 1−ε which embeds into Hilbert space with distortion O(1/ε). This result is best possible and improves part of the metric Ramsey theorem of Bartal, Linial, Mendel and Naor [5], in addition to considerably simplifying its proof. We use our new Ramsey partitions to design the best known approximate distance oracles when the distortion is large, closing a gap left open by Thorup and Zwick in [31]. Namely, we show that for any n point metric space X, and k≥1, there exists an O(k)approximate distance oracle whose storage requirement is O ( n 1+1/k) , and whose query time is a universal constant. We also discuss applications of Ramsey partitions to various other geometric data structure problems, such as the design of efficient data structures for approximate ranking.
Reconstruction using witness complexes
 In Proceedings 18th ACMSIAM Symposium: Discrete Algorithms
, 2007
"... We present a novel reconstruction algorithm that, given an input point set sampled from an object S, builds a oneparameter family of complexes that approximate S at different scales. At a high level, our method is very similar in spirit to Chew’s surface meshing algorithm, with one notable differen ..."
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Cited by 20 (9 self)
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We present a novel reconstruction algorithm that, given an input point set sampled from an object S, builds a oneparameter family of complexes that approximate S at different scales. At a high level, our method is very similar in spirit to Chew’s surface meshing algorithm, with one notable difference though: the restricted Delaunay triangulation is replaced by the witness complex, which makes our algorithm applicable in any metric space. To prove its correctness on curves and surfaces, we highlight the relationship between the witness complex and the restricted Delaunay triangulation in 2d and in 3d. Specifically, we prove that both complexes are equal in 2d and closely related in 3d, under some mild sampling assumptions. 1