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Tight Lower Bounds for DataDependent LocalitySensitive Hashing
, 2015
"... We prove a tight lower bound for the exponent ρ for datadependent LocalitySensitive Hashing schemes, recently used to design efficient solutions for the capproximate nearest neighbor search. In particular, our lower bound matches the bound of ρ ≤ 1 2c−1+o(1) for the `1 space, obtained via the rec ..."
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We prove a tight lower bound for the exponent ρ for datadependent LocalitySensitive Hashing schemes, recently used to design efficient solutions for the capproximate nearest neighbor search. In particular, our lower bound matches the bound of ρ ≤ 1 2c−1+o(1) for the `1 space, obtained via
Localitysensitive hashing scheme based on pstable distributions
 In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry
, 2004
"... inÇÐÓ�Ò We present a novel LocalitySensitive 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 LocalitySensitive 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
Similarity estimation techniques from rounding algorithms
 In Proc. of 34th STOC
, 2002
"... A locality sensitive hashing scheme is a distribution on a family F of hash functions operating on a collection of objects, such that for two objects x, y, Prh∈F[h(x) = h(y)] = sim(x,y), where sim(x,y) ∈ [0, 1] is some similarity function defined on the collection of objects. Such a scheme leads ..."
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Cited by 449 (6 self)
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A locality sensitive hashing scheme is a distribution on a family F of hash functions operating on a collection of objects, such that for two objects x, y, Prh∈F[h(x) = h(y)] = sim(x,y), where sim(x,y) ∈ [0, 1] is some similarity function defined on the collection of objects. Such a scheme leads
The Determinants of Credit Spread Changes.
 Journal of Finance
, 2001
"... ABSTRACT Using dealer's quotes and transactions prices on straight industrial bonds, we investigate the determinants of credit spread changes. Variables that should in theory determine credit spread changes have rather limited explanatory power. Further, the residuals from this regression are ..."
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Cited by 422 (2 self)
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in Slope of Yield Curve Although the spot rate is the only interestratesensitive factor that appears in the firm value process, the spot rate process itself may depend upon other factors as well. 7 For example, Litterman and Scheinkman (1991) find that the two most important factors driving the term
Fast pose estimation with parametersensitive hashing
 In ICCV
, 2003
"... Examplebased methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and highdimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly become pro ..."
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Cited by 250 (8 self)
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prohibitively high. We introduce a new algorithm that learns a set of hashing functions that efficiently index examples in a way relevant to a particular estimation task. Our algorithm extends localitysensitive hashing, a recently developed method to find approximate neighbors in time sublinear in the number
Kernelized localitysensitive hashing for scalable image search
 IEEE International Conference on Computer Vision (ICCV
, 2009
"... Fast retrieval methods are critical for largescale and datadriven vision applications. Recent work has explored ways to embed highdimensional features or complex distance functions into a lowdimensional Hamming space where items can be efficiently searched. However, existing methods do not apply ..."
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Cited by 163 (5 self)
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not apply for highdimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize localitysensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm’s sublinear time similarity search guarantees for a
Smooth ViewDependent LevelofDetail Control and Its Application to Terrain Rendering
"... The key to realtime rendering of largescale surfaces is to locally adapt surface geometric complexity to changing view parameters. Several schemes have been developed to address this problem of viewdependent levelofdetail control. Among these, the viewdependent progressive mesh (VDPM) framewor ..."
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Cited by 264 (1 self)
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The key to realtime rendering of largescale surfaces is to locally adapt surface geometric complexity to changing view parameters. Several schemes have been developed to address this problem of viewdependent levelofdetail control. Among these, the viewdependent progressive mesh (VDPM
Fast pose estimation with parameter sensitive hashing
 In ICCV
, 2003
"... Examplebased methods are effective for parameter estimation problems when the underlying system is simple or the dimensionality of the input is low. For complex and highdimensional problems such as pose estimation, the number of required examples and the computational complexity rapidly become pro ..."
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Cited by 114 (4 self)
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prohibitively high. We introduce a new algorithm that learns a set of hashing functions that efficiently index examples relevant to a particular estimation task. Our algorithm extends a recently developed method for localitysensitive hashing, which finds approximate neighbors in time sublinear in the number
Beyond Locality–Sensitive Hashing
"... We present a new data structure for the c–approximate near neighbor problem (ANN) in the Euclidean space. For n points in Rd, our algorithm achieves Oc(dnρ) query time and Oc(n1+ρ + nd) space, where ρ ≤ 7/(8c2) + O(1/c3) + oc(1). This is the first improvement over the result by Andoni and Indyk (FOC ..."
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Cited by 14 (2 self)
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(FOCS 2006) and the first data structure that bypasses a locality–sensitive hashing lower bound proved by O’Donnell, Wu and Zhou (ITCS 2011). By a standard reduction we obtain a data structure for the Hamming space and ℓ1 norm with ρ ≤ 7/(8c) + O(1/c3/2) + oc(1), which is the first improvement over
Results 1  10
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