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Related papers: Euclidean distance geometry and applications

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By recasting metrical geometry in a purely algebraic setting, both Euclidean and non-Euclidean geometries can be studied over a general field with an arbitrary quadratic form. Both an affine and a projective version of this new theory are…

Metric Geometry · Mathematics 2007-05-23 Norman J. Wildberger

We present alternative postulates for Euclidean geometry whose merit is that they lead to a new class of invariants and associated geometries for real finite-dimensional unital associative algebras.

Rings and Algebras · Mathematics 2025-09-11 Fred Greensite

Finding the point in an algebraic variety that is closest to a given point is an optimization problem with many applications. We study the case when the variety is a Fermat hypersurface. Our formula for its Euclidean distance degree is a…

Algebraic Geometry · Mathematics 2015-10-22 Hwangrae Lee

Although recovering an Euclidean distance matrix from noisy observations is a common problem in practice, how well this could be done remains largely unknown. To fill in this void, we study a simple distance matrix estimate based upon the…

Machine Learning · Statistics 2014-09-18 Luwan Zhang , Grace Wahba , Ming Yuan

This is an attempt to present axioms for Euclidean geometry, aiming at the following goals: to work with geometric notions (thus not merely identify points with pairs of numbers, giving a special status to a particular coordinate system);…

History and Overview · Mathematics 2007-05-23 Eliahu Levy

Data are often represented as graphs. Many common tasks in data science are based on distances between entities. While some data science methodologies natively take graphs as their input, there are many more that take their input in…

Machine Learning · Computer Science 2019-09-19 Leo Liberti

Chamfer distances play an important role in the theory of distance transforms. Though the determination of the exact Euclidean distance transform is also a well investigated area, the classical chamfering method based upon "small"…

Information Theory · Computer Science 2012-01-05 Andras Hajdu , Lajos Hajdu , Robert Tijdeman

This paper addresses to the problem of finding the (minimum) Euclidean distance between two linear varieties. This problem is, usually, solved minimising a target function. We propose a novel approach: to use the Moore-Penrose generalised…

Metric Geometry · Mathematics 2016-11-25 M. A. Facas Vicente , Armando Gonçalves , José Vitória

An isometry is a geometric transformation that preserves distances between pairs of points. We present methods to classify isometries in the Euclidean plane, and extend these methods to spherical, single elliptical, and hyperbolic geometry.…

Metric Geometry · Mathematics 2023-06-28 Lillian MacArthur , Honglin Zhu

Some graphs admit drawings in the Euclidean k-space in such a (natu- ral) way, that edges are represented as line segments of unit length. Such drawings will be called k dimensional unit distance representations. When two non-adjacent…

Combinatorics · Mathematics 2010-01-07 Jan Kratochvil , Boris Horvat , Tomaz Pisanski

We consider logics derived from Euclidean spaces $\mathbb{R}^n$. Each Euclidean space carries relations consisting of those pairs that are, respectively, distance more than 1 apart, distance less than 1 apart, and distance 1 apart. Each…

The concept of $n$-distance was recently introduced to generalize the classical definition of distance to functions of $n$ arguments. In this paper we investigate this concept through a number of examples based on certain geometrical…

Metric Geometry · Mathematics 2023-02-22 Gergely Kiss , Jean-Luc Marichal

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used…

Computer Vision and Pattern Recognition · Computer Science 2023-04-11 Zhi Gao , Chen Xu , Feng Li , Yunde Jia , Mehrtash Harandi , Yuwei Wu

Consider an i.i.d. sample from an unknown density function supported on an unknown manifold embedded in a high dimensional Euclidean space. We tackle the problem of learning a distance between points, able to capture both the geometry of…

Probability · Mathematics 2019-12-30 Pablo Groisman , Matthieu Jonckheere , Facundo Sapienza

Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis),…

Machine Learning · Computer Science 2023-11-22 Fred Lu , Edward Raff , Francis Ferraro

When we represent a network of sensors in Euclidean space by a graph, there are two distances between any two nodes that we may consider. One of them is the Euclidean distance. The other is the distance between the two nodes in the graph,…

Networking and Internet Architecture · Computer Science 2009-06-10 Rodrigo S. C. Leao , Valmir C. Barbosa

Perceptual geometry refers to the interdisciplinary research whose objectives focuses on study of geometry from the perspective of visual perception, and in turn, applies such geometric findings to the ecological study of vision. Perceptual…

Neurons and Cognition · Quantitative Biology 2011-11-29 Arash Sangari , Hasti Mirkia , Amir H. Assadi

A kissing sphere is a sphere that is tangent to a fixed reference ball. We develop in this paper a distance geometry for kissing spheres, which turns out to be a generalization of the classical Euclidean distance geometry.

Metric Geometry · Mathematics 2015-05-04 Hao Chen

The article presents a new approach to euclidean plane geometry based on projective geometric algebra (PGA). It is designed for anyone with an interest in plane geometry, or who wishes to familiarize themselves with PGA. After a brief…

General Mathematics · Mathematics 2016-11-01 Charles G. Gunn

Learning a distance function or metric on a given data manifold is of great importance in machine learning and pattern recognition. Many of the previous works first embed the manifold to Euclidean space and then learn the distance function.…

Machine Learning · Computer Science 2014-05-09 Binbin Lin , Ji Yang , Xiaofei He , Jieping Ye