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101,506
Smooth Localized Orthonormal Bases
, 1992
"... . We describe an orthogonal decomposition of L 2 (R) which maps smooth functions to smooth periodic functions. It generalizes previous constructions by Malvar, Coifman and Meyer. The adjoint of the decomposition can be used to construct smooth orthonormal windowed exponential, wavelet and wavelet ..."
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Cited by 9 (0 self)
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. We describe an orthogonal decomposition of L 2 (R) which maps smooth functions to smooth periodic functions. It generalizes previous constructions by Malvar, Coifman and Meyer. The adjoint of the decomposition can be used to construct smooth orthonormal windowed exponential, wavelet and wavelet
On Local Orthonormal Bases For Classification And Regression
 Comptes Rendus Acad. Sci. Paris, Serie I
, 1995
"... We describe extensions to the "bestbasis" method to select orthonormal bases suitable for signal classification and regression problems from a large collection of orthonormal bases. For classification problems, we select the basis which maximizes relative entropy of timefrequency energy ..."
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We describe extensions to the "bestbasis" method to select orthonormal bases suitable for signal classification and regression problems from a large collection of orthonormal bases. For classification problems, we select the basis which maximizes relative entropy of timefrequency energy
Fast Computational Algorithms for the Discrete Wavelet Transform and Applications of Localized Orthonormal Bases in Signal Classification
, 1997
"... In the first part of this paper we construct an algorithm for implementing the discrete wavelet transform by means of matrices in SO 2 (R) for orthonormal compactly supported wavelets and matrices in SLm (R), m for compactly supported biorthogonal wavelets. We show that in 1 dimension the total op ..."
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Cited by 4 (1 self)
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of training signals using the LDBalgorithm of N.Saito and R.Coifman. In this analysis we consider several dictionaries of orthonormal bases. The resulting most discriminating basis functions are used to construct classifiers. The success of di#erent dictionaries is measured by c...
Orthonormal bases of compactly supported wavelets
, 1993
"... Several variations are given on the construction of orthonormal bases of wavelets with compact support. They have, respectively, more symmetry, more regularity, or more vanishing moments for the scaling function than the examples constructed in Daubechies [Comm. Pure Appl. Math., 41 (1988), pp. 90 ..."
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Cited by 2205 (27 self)
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Several variations are given on the construction of orthonormal bases of wavelets with compact support. They have, respectively, more symmetry, more regularity, or more vanishing moments for the scaling function than the examples constructed in Daubechies [Comm. Pure Appl. Math., 41 (1988), pp
Advances in Computational Mathematics 20: 367–384, 2004. © 2004 Kluwer Academic Publishers. Printed in the Netherlands. The matrixvalued Riesz lemma and local orthonormal bases in shiftinvariant spaces
, 2002
"... We use the matrixvalued Fejér–Riesz lemma for Laurent polynomials to characterize when a univariate shiftinvariant space has a local orthonormal shiftinvariant basis, and we apply the above characterization to study local dual frame generators, local orthonormal bases of wavelet spaces, and MRAb ..."
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We use the matrixvalued Fejér–Riesz lemma for Laurent polynomials to characterize when a univariate shiftinvariant space has a local orthonormal shiftinvariant basis, and we apply the above characterization to study local dual frame generators, local orthonormal bases of wavelet spaces, and MRAbased
LOF: Identifying densitybased local outliers
 MOD
, 2000
"... For many KDD applications, such as detecting criminal activities in Ecommerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for ..."
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Cited by 516 (13 self)
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that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal
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
A Data Locality Optimizing Algorithm
, 1991
"... This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation algorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory that unifi ..."
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Cited by 804 (16 self)
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This paper proposes an algorithm that improves the locality of a loop nest by transforming the code via interchange, reversal, skewing and tiling. The loop transformation algorithm is based on two concepts: a mathematical formulation of reuse and locality, and a loop transformation theory
Local grayvalue invariants for image retrieval
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1997
"... Abstract—This paper addresses the problem of retrieving images from large image databases. The method is based on local grayvalue invariants which are computed at automatically detected interest points. A voting algorithm and semilocal constraints make retrieval possible. Indexing allows for efficie ..."
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Cited by 548 (27 self)
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Abstract—This paper addresses the problem of retrieving images from large image databases. The method is based on local grayvalue invariants which are computed at automatically detected interest points. A voting algorithm and semilocal constraints make retrieval possible. Indexing allows
A PERFORMANCE EVALUATION OF LOCAL DESCRIPTORS
, 2005
"... In this paper we compare the performance of descriptors computed for local interest regions, as for example extracted by the HarrisAffine detector [32]. Many different descriptors have been proposed in the literature. However, it is unclear which descriptors are more appropriate and how their perfo ..."
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Cited by 1783 (51 self)
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In this paper we compare the performance of descriptors computed for local interest regions, as for example extracted by the HarrisAffine detector [32]. Many different descriptors have been proposed in the literature. However, it is unclear which descriptors are more appropriate and how
Results 1  10
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101,506