Affinity and Distance. On the Newtonian Structure of Some Data Kernels

Pub Date : 2018-06-01 DOI:10.1515/agms-2018-0005
H. Aimar, I. Gómez
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引用次数: 1

Abstract

Abstract Let X be a set. Let K(x, y) > 0 be a measure of the affinity between the data points x and y. We prove that K has the structure of a Newtonian potential K(x, y) = φ(d(x, y)) with φ decreasing and d a quasi-metric on X under two mild conditions on K. The first is that the affinity of each x to itself is infinite and that for x ≠ y the affinity is positive and finite. The second is a quantitative transitivity; if the affinity between x and y is larger than λ > 0 and the affinity of y and z is also larger than λ, then the affinity between x and z is larger than ν(λ). The function ν is concave, increasing, continuous from R+ onto R+ with ν(λ) < λ for every λ > 0
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亲和和距离。关于某些数据核的牛顿结构
设X是一个集合。设K(x, y) > 0是数据点x和y之间亲和力的度量。我们证明了K在两个温和条件下具有牛顿势K(x, y) = φ(d(x, y))的结构,并且在K上证明了d是x上的一个准度量。首先,每个x对自身的亲和力是无限的,当x≠y时,亲和力是正有限的。第二个是数量及物性;如果x与y的亲和力大于λ >, y与z的亲和力也大于λ,则x与z的亲和力大于ν(λ)。函数ν是凹的,递增的,从R+到R+连续的,且对于每一个λ >, ν(λ) < λ
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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