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More Sophisticated Is Not Always Better: A Comparison of Similarity Measures for Unsupervised Learning of Pathways in Biomolecular Simulations. [PDF]
J Phys Chem BJäger M, Wolf S.
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Structured generative modelling of earthquake response spectra with hierarchical latent variables in hyperbolic geometry. [PDF]
Sci RepWright A, Fayaz J.
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Proceedings of the 2014 Symposium on Symbolic-Numeric Computation, 2014
The nearest point map of a real algebraic variety with respect to Euclidean distance is an algebraic function. For instance, for varieties of low rank matrices, the Eckart-Young Theorem states that this map is given by the singular value decomposition.
Draisma, J. +4 more
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The nearest point map of a real algebraic variety with respect to Euclidean distance is an algebraic function. For instance, for varieties of low rank matrices, the Eckart-Young Theorem states that this map is given by the singular value decomposition.
Draisma, J. +4 more
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On Euclidean Distances and Sphere Representations
Graphs and Combinatorics, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Ridge points in Euclidean distance maps
Pattern Recognition Letters, 1992Abstract Two types of ridge points are identified on the Euclidean distance map of a digital object. From this set, which is connected, the skeleton of the object is derived as a unit width subset. The use of the Euclidean distance map guarantees obtaining a skeleton with structure sufficiently stable under object rotation, and placed where it is ...
C Arcelli, G Sanniti di Baja
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A Parallel Euclidean Distance Transformation Algorithm
Computer Vision and Image Understanding, 1996We present a parallel algorithm for the Euclidean distance transformation (EDT). It is a “divide-and-conquer” algorithm based on a fast sequential algorithm for the signed EDT (SEDT). The combining step that follows the local partial calculation of the SEDT can be done efficiently after reformulating the SEDT problem as the partial calculation of a ...
H. Embrechts, D. Roose
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Minisum Location with Closest Euclidean Distances
Annals of Operations Research, 2002In previous papers, the authors of this paper formulated a single facility minisum location problem, where the set of customers as well as the new facility may be represented as areas on the plane and the rectangular norm was used as the distance function [see Naval Res. Logist. 47, 77--84 (2000; Zbl 0953.90033) and Comput. Oper. Res.
Brimberg, J., Wesolowsky, G. O.
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2011
The Euclidean distance function is very important in instrumentation circuits, communication, neural networks, display systems or classification algorithms, useful for vector quantization or nearest neighbor classification. In order to obtain a good frequency response, the Euclidean distance circuits are implemented using exclusively MOS transistors ...
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The Euclidean distance function is very important in instrumentation circuits, communication, neural networks, display systems or classification algorithms, useful for vector quantization or nearest neighbor classification. In order to obtain a good frequency response, the Euclidean distance circuits are implemented using exclusively MOS transistors ...
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The Mathematics Teacher, 1971
There are basically two approaches to classical Euclidean plane geometry—the synthetic approach and the metric approach. The older of the two is the synthetic approach followed by Eucliding later by Hilbert. In the Eucliding treatment, one begin by assuming as undefined the relations of betweenness, congruence of segments, and congruence of angles. The
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There are basically two approaches to classical Euclidean plane geometry—the synthetic approach and the metric approach. The older of the two is the synthetic approach followed by Eucliding later by Hilbert. In the Eucliding treatment, one begin by assuming as undefined the relations of betweenness, congruence of segments, and congruence of angles. The
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On the Euclidean distance of images
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005We present a new Euclidean distance for images, which we call IMage Euclidean Distance (IMED). Unlike the traditional Euclidean distance, IMED takes into account the spatial relationships of pixels. Therefore, it is robust to small perturbation of images. We argue that IMED is the only intuitively reasonable Euclidean distance for images.
Liwei, Wang, Yan, Zhang, Jufu, Feng
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