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In this work, a metric is presented on the set of boundedly-compact pointed metric spaces that generates the Gromov-Hausdorff topology. A similar metric is defined for measured metric spaces that generates the Gromov-Hausdorff-Prokhorov…

Metric Geometry · Mathematics 2020-01-10 Ali Khezeli

Let $X$ be a (data) 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 $K(x,y)=\varphi(d(x,y))$ with $\varphi$ decreasing and $d$ a quasi-metric…

General Topology · Mathematics 2017-01-16 Hugo Aimar , Ivana Gómez

We give a dimensionality reduction procedure to approximate the sum of distances of a given set of $n$ points in $R^d$ to any "shape" that lies in a $k$-dimensional subspace. Here, by "shape" we mean any set of points in $R^d$. Our…

Data Structures and Algorithms · Computer Science 2021-06-25 Zhili Feng , Praneeth Kacham , David P. Woodruff

In this manuscript, we claim that the newly introduced $\mathcal{F}$-metric space \cite[\, M.~Jleli and B.~Samet, On a new generalization of metric spaces, J. Fixed Point Theory Appl, 20(3) 2018]{JS1} is metrizable. Also, we deduce that the…

Functional Analysis · Mathematics 2018-08-09 Sumit Som , Ashis Bera , Lakshmi Kanta Dey

We define metrics on Culler-Vogtmann space, which are an analogue of the Teichmuller metric and are constructed using stretching factors. In fact the metrics we study are related, one being a symmetrised version of the other. We investigate…

Group Theory · Mathematics 2011-07-22 Stefano Francaviglia , Armando Martino

Let $X$ be a compact K\"ahler manifold. Given a big cohomology class $\{\theta\}$, there is a natural equivalence relation on the space of $\theta$-psh functions giving rise to $\mathcal S(X,\theta)$, the space of singularity types of…

Differential Geometry · Mathematics 2023-09-19 Tamás Darvas , Eleonora Di Nezza , Chinh H. Lu

For a (compact) subset $K$ of a metric space and $\varepsilon > 0$, the {\em covering number} $N(K , \varepsilon )$ is defined as the smallest number of balls of radius $\varepsilon$ whose union covers $K$. Knowledge of the {\em metric…

Metric Geometry · Mathematics 2009-09-25 Stanislaw J. Szarek

Contact metric $(\kappa ,\mu )$-spaces are generalizations of Sasakian spaces. We introduce a weak $(\kappa ,\mu )$ condition as a generalization of the K-contact one and show that many of the known results from generalized Sasakian…

Differential Geometry · Mathematics 2022-07-15 Philippe Rukimbira

The Metric $k$-median problem over a metric space $(\mathcal{X}, d)$ is defined as follows: given a set $L \subseteq \mathcal{X}$ of facility locations and a set $C \subseteq \mathcal{X}$ of clients, open a set $F \subseteq L$ of $k$…

Data Structures and Algorithms · Computer Science 2020-07-24 Dishant Goyal , Ragesh Jaiswal , Amit Kumar

Suppose $(X,\omega)$ is a compact K\"ahler manifold of dimension $n$, and $\theta$ is closed $(1,1)$-form representing a big cohomology class. We introduce a metric $d_1$ on the finite energy space $\mathcal{E}^1(X,\theta)$, making it a…

Differential Geometry · Mathematics 2023-09-19 Tamás Darvas , Eleonora Di Nezza , Chinh H. Lu

In this paper, we first discussed multiplicative metric mapping by giving some topological properties of the relevant multiplicative metric space. As an interesting result of our discussions, we observed that the set of positive real…

General Mathematics · Mathematics 2014-10-13 Muttalip Ozavsar , Adem Cengiz Cevikel

Kernel mean embeddings have recently attracted the attention of the machine learning community. They map measures $\mu$ from some set $M$ to functions in a reproducing kernel Hilbert space (RKHS) with kernel $k$. The RKHS distance of two…

Machine Learning · Statistics 2019-12-18 Carl-Johann Simon-Gabriel , Bernhard Schölkopf

The k-means problem consists of finding k centers in the d-dimensional Euclidean space that minimize the sum of the squared distances of all points in an input set P to their closest respective center. Awasthi et. al. recently showed that…

Computational Geometry · Computer Science 2015-09-04 Euiwoong Lee , Melanie Schmidt , John Wright

Let $G$ be a connected graph and let $d_G$ be the geodesic distance on $V(G)$. The metric spaces $(V(G), d_{G})$ are characterized up to isometry for all finite connected $G$ by David C. Kay and Gary Chartrand in 1964. The main result of…

Combinatorics · Mathematics 2024-11-05 Oleksiy Dovgoshey

Graphs are used in almost every scientific discipline to express relations among a set of objects. Algorithms that compare graphs, and output a closeness score, or a correspondence among their nodes, are thus extremely important. Despite…

Discrete Mathematics · Computer Science 2020-11-17 Sam Safavi , José Bento

Let $(X,d)$ be a compact metric space. We consider the behavior of probability measures $\mu$ with the property that $$ \int_{X} d(x, y) d\mu(y) \qquad \mbox{is independent of}~x \in X.$$ It appears that such measures, when they exist,…

Metric Geometry · Mathematics 2026-02-24 Stefan Steinerberger

These informal notes deal with some basic properties of metric spaces, especially concerning lengths of curves.

Metric Geometry · Mathematics 2007-09-27 Stephen Semmes

This article concerns a class of metric spaces, which we call multigeodesic spaces, where between any two distinct points there exist multiple distinct minimising geodesics. We provide a simple characterisation of multigeodesic normed…

Metric Geometry · Mathematics 2025-05-07 Amlan Banaji

Consider a divisor D with simple normal crossings in a compact K\"ahler manifold X. We show in this article that a K\"ahler metric in an arbitrary class, with constant scalar curvature and cusp singularities along the divisor is unique in…

Differential Geometry · Mathematics 2011-09-20 Hugues Auvray

Metric space magnitude, an active subject of research in algebraic topology, originally arose in the context of biology, where it was used to represent the effective number of distinct species in an environment. In a more general setting,…

Machine Learning · Computer Science 2020-07-06 Eric Bunch , Daniel Dickinson , Jeffery Kline , Glenn Fung
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