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3,149
Dynamic programming algorithm optimization for spoken word recognition
 IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
, 1978
"... This paper reports on an optimum dynamic programming (DP) based timenormalization algorithm for spoken word recognition. First, a general principle of timenormalization is given using timewarping function. Then, two timenormalized distance definitions, ded symmetric and asymmetric forms, are der ..."
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Cited by 788 (3 self)
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This paper reports on an optimum dynamic programming (DP) based timenormalization algorithm for spoken word recognition. First, a general principle of timenormalization is given using timewarping function. Then, two timenormalized distance definitions, ded symmetric and asymmetric forms
Fuzzy extractors: How to generate strong keys from biometrics and other noisy data
, 2008
"... We provide formal definitions and efficient secure techniques for • turning noisy information into keys usable for any cryptographic application, and, in particular, • reliably and securely authenticating biometric data. Our techniques apply not just to biometric information, but to any keying mater ..."
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Cited by 535 (38 self)
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We provide formal definitions and efficient secure techniques for • turning noisy information into keys usable for any cryptographic application, and, in particular, • reliably and securely authenticating biometric data. Our techniques apply not just to biometric information, but to any keying
Interpolation of Scattered Data: Distance Matrices and Conditionally Positive Definite Functions
 CONSTRUCTIVE APPROXIMATION
, 1986
"... Among other things, we prove that multiquadric surface interpolation is always solvable, thereby settling a conjecture of R. Franke. ..."
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Cited by 359 (3 self)
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Among other things, we prove that multiquadric surface interpolation is always solvable, thereby settling a conjecture of R. Franke.
Principal Curves
, 1989
"... Principal curves are smooth onedimensional curves that pass through the middle of a pdimensional data set, providing a nonlinear summary of the data. They are nonparametric, and their shape is suggested by the data. The algorithm for constructing principal curve starts with some prior summary, suc ..."
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Cited by 394 (1 self)
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, such as the usual principalcomponent line. The curve in each successive iteration is a smooth or local average of the pdimensional points, where the definition of local is based on the distance in arc length of the projections of the points onto the curve found in the previous iteration. In this article principal
A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features
 Machine Learning
, 1993
"... In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of t ..."
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Cited by 309 (3 self)
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In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment
Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
 Proceedings of the National Academy of Sciences
, 2005
"... of contexts of data analysis, such as spectral graph theory, manifold learning, nonlinear principal components and kernel methods. We augment these approaches by showing that the diffusion distance is a key intrinsic geometric quantity linking spectral theory of the Markov process, Laplace operators ..."
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Cited by 257 (45 self)
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of contexts of data analysis, such as spectral graph theory, manifold learning, nonlinear principal components and kernel methods. We augment these approaches by showing that the diffusion distance is a key intrinsic geometric quantity linking spectral theory of the Markov process, Laplace
Metric spaces and positive definite functions
 Transactions of the American Mathematical Society
, 1938
"... generally EmP the pseudoeuclidean space of m real variables with the distance function p> o. As p ~ 00 we get the space E = with the distance function maXi=l,...,m IXi xl I. ..."
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Cited by 194 (0 self)
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generally EmP the pseudoeuclidean space of m real variables with the distance function p> o. As p ~ 00 we get the space E = with the distance function maXi=l,...,m IXi xl I.
Region Covariance: A Fast Descriptor for Detection And Classification
 In Proc. 9th European Conf. on Computer Vision
, 2006
"... We describe a new region descriptor and apply it to two problems, object detection and texture classification. The covariance of dfeatures, e.g., the threedimensional color vector, the norm of first and second derivatives of intensity with respect to x and y, etc., characterizes a region of in ..."
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Cited by 278 (14 self)
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. Covariance matrices do not lie on Euclidean space, therefore we use a distance metric involving generalized eigenvalues which also follows from the Lie group structure of positive definite matrices. Feature matching is a simple nearest neighbor search under the distance metric and performed extremely
A New Theory of DeadlockFree Adaptive Routing in Wormhole Networks
 IEEE Transactions on Parallel and Distributed Systems
, 1993
"... Abstract Second generation multicomputers use wormhole routing, allowing a very low channel setup time and drastically reducing the dependency between network latency and internode distance. Deadlockfree routing strategies have been developed, allowing the implementation of fast hardware routers t ..."
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Cited by 261 (28 self)
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Abstract Second generation multicomputers use wormhole routing, allowing a very low channel setup time and drastically reducing the dependency between network latency and internode distance. Deadlockfree routing strategies have been developed, allowing the implementation of fast hardware routers
Definition
"... The iDistance is an indexing and query processing technique for k nearest neighbor (kNN) queries on point data in multidimensional metric spaces. The kNN query is one of the hardest problems on multidimensional data. It has been shown analytically and experimentally that any algorithm using hierar ..."
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The iDistance is an indexing and query processing technique for k nearest neighbor (kNN) queries on point data in multidimensional metric spaces. The kNN query is one of the hardest problems on multidimensional data. It has been shown analytically and experimentally that any algorithm using
Results 1  10
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3,149