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Related papers: Limitations to Frechet's Metric Embedding Method

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We show that for every nondecreasing concave function w:R+ --> R+ with w(0)=0, either every finite metric space embeds with distortion arbitrarily close to 1 into a metric space of the form (X,w o d) for some metric d on X, or there exists…

Metric Geometry · Mathematics 2011-02-10 Manor Mendel , Assaf Naor

It is shown that any bounded metric space can be isometrically embedded into the Gromov--Hausdorff metric class GH. This result is a consequence of local geometry description of the class GH in a sufficiently small neighborhood of a generic…

Metric Geometry · Mathematics 2022-03-08 Alexander O. Ivanov , Alexey A. Tuzhilin

We present a new numerical method for the isometric embedding of 2-geometries specified by their 2-metrics in three dimensional Euclidean space. Our approach is to directly solve the fundamental embedding equation supplemented by six…

General Relativity and Quantum Cosmology · Physics 2015-06-23 Wolfgang Tichy , Jonathan R. McDonald , Warner A. Miller

We establish finite-sample error bounds in expectation for transformed Fr\'echet means in Hadamard spaces under minimal assumptions. Transformed Fr\'echet means provide a unifying framework encompassing classical and robust notions of…

Statistics Theory · Mathematics 2025-11-11 Christof Schötz

We propose an approach for capturing the signal variability in hyperspectral imagery using the framework of the Grassmann manifold. Labeled points from each class are sampled and used to form abstract points on the Grassmannian. The…

Computer Vision and Pattern Recognition · Computer Science 2015-02-04 Sofya Chepushtanova , Michael Kirby

We introduce and study finite $d$-volumes - the high dimensional generalization of finite metric spaces. Having developed a suitable combinatorial machinery, we define $\ell_1$-volumes and show that they contain Euclidean volumes and…

Data Structures and Algorithms · Computer Science 2010-08-03 Ilan Newman , Yuri Rabinovich

For a given metric measure space $(X,d,\mu)$ we consider finite samples of points, calculate the matrix of distances between them and then reconstruct the points in some finite-dimensional space using the multidimensional scaling (MDS)…

Metric Geometry · Mathematics 2022-08-02 Alexey Kroshnin , Eugene Stepanov , Dario Trevisan

In this paper, we study the weighted Fermat-Frechet problem for a $\frac{N (N+1)}{2}-$tuple of positive real numbers determining $N$-simplexes in the $N$ dimensional $K$-Space ($N$-dimensional Euclidean space $\mathbb{R}^{N}$ if $K=0,$ the…

Differential Geometry · Mathematics 2022-09-20 Anastasios N. Zachos

Local Linear embedding (LLE) is a popular dimension reduction method. In this paper, we first show LLE with nonnegative constraint is equivalent to the widely used Laplacian embedding. We further propose to iterate the two steps in LLE…

Machine Learning · Computer Science 2012-07-03 Deguang Kong , Chris H. Q. Ding , Heng Huang , Feiping Nie

We give a fast oblivious L2-embedding of $A\in \mathbb{R}^{n x d}$ to $B\in \mathbb{R}^{r x d}$ satisfying $(1-\varepsilon)\|A x\|_2^2 \le \|B x\|_2^2 <= (1+\varepsilon) \|Ax\|_2^2.$ Our embedding dimension $r$ equals $d$, a constant…

Machine Learning · Computer Science 2019-09-30 Malik Magdon-Ismail , Alex Gittens

In this paper we study {\em terminal embeddings}, in which one is given a finite metric $(X,d_X)$ (or a graph $G=(V,E)$) and a subset $K \subseteq X$ of its points are designated as {\em terminals}. The objective is to embed the metric into…

Data Structures and Algorithms · Computer Science 2016-03-09 Michael Elkin , Arnold Filtser , Ofer Neiman

The metric dimension reduction modulus $k^\alpha_n(\ell_\infty)$ is the smallest $k$ such that every $n$--point metric space can be embedded into some $k$-dimensional normed space, with bi--Lipschitz distortion at most $\alpha$. Determining…

Metric Geometry · Mathematics 2025-08-12 Dylan J. Altschuler , Konstantin Tikhomirov

We obtain new oscillation inequalities in metric spaces in terms of the Peetre $K-$functional and the isoperimetric profile. Applications provided include a detailed study of Fractional Sobolev inequalities and the Morrey-Sobolev embedding…

Functional Analysis · Mathematics 2014-04-01 Joaquim Martin , Mario Milman

A general fixed point theorem for isometries in terms of metric functionals is proved under the assumption of the existence of a conical bicombing. It is new even for isometries of Banach spaces as well as for non-locally compact…

Functional Analysis · Mathematics 2023-01-19 Anders Karlsson

We study the non-compact Sobolev embeddings into the optimal scale of Lorentz spaces, $W_0^mL^{p,q}(\Omega) \to L^{\frac{dp}{d - mp},r}(\Omega)$, where $\Omega \subseteq \mathbb{R}^d$, $1 \le m \le d$ and $0<q<r\le\infty$ with $1<p<\frac…

Functional Analysis · Mathematics 2025-02-11 Chian Yeong Chuah , Jan Lang , Liding Yao

For $p\in (1,\infty)$ let $\mathscr{P}_p(\mathbb{R}^3)$ denote the metric space of all $p$-integrable Borel probability measures on $\mathbb{R}^3$, equipped with the Wasserstein $p$ metric $\mathsf{W}_p$. We prove that for every…

Metric Geometry · Mathematics 2015-09-30 Alexandr Andoni , Assaf Naor , Ofer Neiman

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

We study the problem of construction of explicit isometric embeddings of (pseudo)-Riemannian manifolds. We discuss the method which is based in the idea that the exterior symmetry of the embedded surface and the interior symmetry of the…

General Relativity and Quantum Cosmology · Physics 2020-12-17 A. A. Sheykin , M. V. Markov , Ya. A. Fedulov , S. A. Paston

We produce a series of Central Limit Theorems (CLTs) associated to compact metric measure spaces $(K,d,\eta)$, with $\eta$ a reasonable probability measure. For the first CLT, we can ignore $\eta$ by isometrically embedding $K$ into…

Probability · Mathematics 2020-01-14 Steven Rosenberg , Jie Xu

Ordinal embedding aims at finding a low dimensional representation of objects from a set of constraints of the form "item $j$ is closer to item $i$ than item $k$". Typically, each object is mapped onto a point vector in a low dimensional…

Machine Learning · Computer Science 2021-05-26 Aïssatou Diallo , Johannes Fürnkranz