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Related papers: The $k$-Fr\'echet distance

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This paper explores the relationship between closed curves on surfaces and their intersections. Like Dehn-Thurston coordinates for simple curves, we explore how to determine closed curves using the number of times they intersect other…

Geometric Topology · Mathematics 2023-08-29 Hugo Parlier , Binbin Xu

In this paper, we revisit the notion of length measures associated to planar closed curves. These are a special case of area measures of hypersurfaces which were introduced early on in the field of convex geometry. The length measure of a…

Differential Geometry · Mathematics 2020-10-28 Nicolas Charon , Thomas Pierron

This paper contains a comparative study of two families of simple curves drawn in the plane. On the one hand, we have the fractal curves on the unit interval, with self-similar structure, which have associated a Hausdorff dimension. On the…

Classical Analysis and ODEs · Mathematics 2015-04-07 R. Hansen , M. Piacquadio

The $k$-nearest neighbour ($k$-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the $k$-NN classifier. In this…

Machine Learning · Statistics 2015-12-02 Stan Hatko

Comparing two geometric graphs embedded in space is important in the field of transportation network analysis. Given street maps of the same city collected from different sources, researchers often need to know how and where they differ.…

Computational Geometry · Computer Science 2015-02-17 Mahmuda Ahmed , Brittany Terese Fasy , Kyle S. Hickmann , Carola Wenk

In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of fixed anchor points. Then, we define the distance in the…

Machine Learning · Computer Science 2014-05-16 Jun Wang , Ke Sun , Fei Sha , Stephane Marchand-Maillet , Alexandros Kalousis

The k Nearest Neighbors (kNN) method has received much attention in the past decades, where some theoretical bounds on its performance were identified and where practical optimizations were proposed for making it work fairly well in high…

Machine Learning · Computer Science 2016-06-14 Aleksander Lodwich , Faisal Shafait , Thomas Breuel

A $k$-truss is an edge-induced subgraph $H$ such that each of its edges belongs to at least $k-2$ triangles of $H$. This notion has been introduced around ten years ago in social network analysis and security, as a form of cohesive subgraph…

Data Structures and Algorithms · Computer Science 2020-10-05 Alessio Conte , Roberto Grossi , Andrea Marino , Luca Versari

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

Given a directed graph $G = (V, E)$, the $k$-path partition problem is to find a minimum collection of vertex-disjoint directed paths each of order at most $k$ to cover all the vertices of $V$. The problem has various applications in…

Data Structures and Algorithms · Computer Science 2021-07-13 Yong Chen , Zhi-Zhong Chen , Curtis Kennedy , Guohui Lin , Yao Xu , An Zhang

We describe a polynomial-time algorithm to compute a (tight) geodesic between two curves in the curve graph. As well as enabling us to compute the distance between a pair of curves, this has several applications to mapping classes. For…

Geometric Topology · Mathematics 2016-10-05 Mark C. Bell , Richard C. H. Webb

This document reviews the definition of the kernel distance, providing a gentle introduction tailored to a reader with background in theoretical computer science, but limited exposure to technology more common to machine learning,…

Computational Geometry · Computer Science 2011-03-11 Jeff M. Phillips , Suresh Venkatasubramanian

Given a redundant dictionary $\Phi$, represented by an $M \times N$ matrix ($\Phi \in \mathbb{R}^{M \times N}$) and a target signal $y \in \mathbb{R}^M$, the \emph{sparse approximation problem} asks to find an approximate representation of…

Computational Complexity · Computer Science 2011-11-29 Ali Civril

We propose a family of near-metrics based on local graph diffusion to capture similarity for a wide class of data sets. These quasi-metametrics, as their names suggest, dispense with one or two standard axioms of metric spaces, specifically…

Machine Learning · Statistics 2017-10-18 Chu Wang , Iraj Saniee , William S. Kennedy , Chris A. White

A quasi-metric is a distance function which satisfies the triangle inequality but is not symmetric: it can be thought of as an asymmetric metric. The central result of this thesis, developed in Chapter 3, is that a natural correspondence…

Information Retrieval · Computer Science 2008-10-31 Aleksandar Stojmirovic

The closest pair problem is a fundamental problem of computational geometry: given a set of $n$ points in a $d$-dimensional space, find a pair with the smallest distance. A classical algorithm taught in introductory courses solves this…

Quantum Physics · Physics 2020-08-07 Scott Aaronson , Nai-Hui Chia , Han-Hsuan Lin , Chunhao Wang , Ruizhe Zhang

In this paper we introduce the notion of rational Hausdorff divisor, we analyze the dimension and irreducibility of its associated linear system of curves, and we prove that all irreducible real curves belonging to the linear system are…

Algebraic Geometry · Mathematics 2014-01-22 Sonia L. Rueda , Juana Sendra , J. Rafael Sendra

We give algorithms to compute the Fr\'echet distance of trees and graphs with bounded tree width. Our algorithms run in $O(n^2)$ time for trees of bounded degree, and $O(n^2\sqrt{n \log n})$ time for trees of arbitrary degree. For graphs of…

Computational Geometry · Computer Science 2020-01-29 Maike Buchin , Amer Krivošija , Alexander Neuhaus

In this paper we present algorithms for a number of problems in geometric pattern matching where the input consist of a collections of segments in the plane. Our work consists of two main parts. In the first, we address problems and…

Computational Geometry · Computer Science 2007-05-23 Alon Efrat , Piotr Indyk , Suresh Venkatasubramanian

While there is extensive literature on approximation, deterministic as well as random, of general convex bodies $K$ in the symmetric difference metric, or other metrics arising from intrinsic volumes, very little is known for corresponding…

Metric Geometry · Mathematics 2025-08-25 Joscha Prochno , Carsten Schütt , Mathias Sonnleitner , Elisabeth M. Werner
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