info:eu-repo/semantics/article
Affinity and distance. On the Newtonian structure of some data kernels
Fecha
2018-02Registro en:
Aimar, Hugo Alejandro; Gomez, Ivana Daniela; Affinity and distance. On the Newtonian structure of some data kernels; De Gruyter Open Ltd; Analysis and Geometry in Metric Spaces; 6; 1; 2-2018; 89-95
2299-3274
2299-3274
CONICET Digital
CONICET
Autor
Aimar, Hugo Alejandro
Gomez, Ivana Daniela
Resumen
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.