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We consider the problems of clustering, classification, and visualization of high-dimensional data when no straightforward Euclidean representation exists. Typically, these tasks are performed by first reducing the high-dimensional data to…

Machine Learning · Statistics 2009-09-29 Kevin M. Carter , Raviv Raich , William G. Finn , Alfred O. Hero

Scaffolds are certain tensors arising in the study of association schemes, and have been (implicitly) understood diagrammatically as digraphs with distinguished "root" nodes and with matrix edge weights, often taken from Bose-Mesner…

Combinatorics · Mathematics 2022-01-05 Xiaoye Liang , Ying-Ying Tan , Hajime Tanaka , Tao Wang

This paper is about Information Geometry, a relatively new subject within mathematical statistics that attempts to study the problem of inference by using tools from modern differential geometry. This paper provides an overview of some of…

Data Analysis, Statistics and Probability · Physics 2007-05-23 Carlos C. Rodriguez

We study information-theoretic phase transitions for the detectability of latent geometry in bipartite random geometric graphs RGGs with Gaussian d-dimensional latent vectors while only a subset of edges carries latent information…

Probability · Mathematics 2026-04-13 Andreas Göbel , Marcus Pappik , Leon Schiller

A Pfaff field on a projective space is a map from the sheaf of differential s-forms, for a certain s, to an invertible sheaf. The interesting ones are those arising from a Pfaff system, as they give rise to a distribution away from their…

Algebraic Geometry · Mathematics 2009-02-16 Joana D. A. S. Cruz , Eduardo Esteves

We define a notion of global analytic space with overconvergent structure sheaf. This gives an analog on a general base Banach ring of Grosse-Kloenne's overconvergent p-adic spaces and of Bambozzi's generalized affinoid varieties over R.…

Algebraic Geometry · Mathematics 2015-02-09 Frédéric Paugam

Classes of branched surfaces extend the classes of surfaces or 2-dimensional manifolds satisfying suitable properties and defined in various manners. Reeb spaces of smooth maps of suitable classes into surfaces whose codimensions are…

General Topology · Mathematics 2022-08-16 Naoki Kitazawa

We prove implicit function theorems for mappings on topological vector spaces over valued fields. In the real and complex cases, we obtain implicit function theorems for mappings from arbitrary (not necessarily locally convex) topological…

General Mathematics · Mathematics 2007-05-23 Helge Glockner

This paper develops a geometric approach of variational analysis for the case of convex objects considered in locally convex topological spaces and also in Banach space settings. Besides deriving in this way new results of convex calculus,…

Optimization and Control · Mathematics 2017-05-12 Boris Mordukhovich , Nguyen Mau Nam , R. Blake Rector , Tuyen Tran

Much of arithmetic geometry is concerned with the study of principal bundles. They occur prominently in the arithmetic of elliptic curves and, more recently, in the study of the Diophantine geometry of curves of higher genus. In particular,…

Number Theory · Mathematics 2018-10-17 Minhyong Kim

We propose an integral geometric approach for computing dual distributions for the parameter distributions of multilinear models. The dual distributions can be computed from, for example, the parameter distributions of conics, multiple view…

Computer Vision and Pattern Recognition · Computer Science 2018-12-04 Sami Sebastian Brandt

A frame independent formulation of analytical mechanics in the Newtonian space-time is presented The differential geometry of affine values i.e., the differential geometry in which affine bundles replace vector bundles and sections of one…

Mathematical Physics · Physics 2009-11-11 Katarzyna Grabowska , Pawel Urbanski

In this paper we investigate the geometry of the likelihood of the unknown parameters in a simple class of Bayesian directed graphs with hidden variables. This enables us, before any numerical algorithms are employed, to obtain certain…

Machine Learning · Computer Science 2013-02-01 Raffaella Settimi , Jim Q. Smith

Post-data statistical inference concerns making probability statements about model parameters conditional on observed data. When a priori knowledge about parameters is available, post-data inference can be conveniently made from Bayesian…

Statistics Theory · Mathematics 2025-06-05 Yang Liu , Jan Hannig , Alexander C Murph

We introduce a new language to describe the geometry of affine Deligne-Lusztig varieties in affine flag varieties. This second part of a two paper series uses this new language, i.e. the double Bruhat graph, to describe certain structure…

Representation Theory · Mathematics 2025-09-10 Felix Schremmer

For a map $f:X \to M$ into a manifold $M$, we study the sets of deficient and multiple points of $f$. In case of the set of deficient points, we estimate its dimension. For multiple points, we study its density in $X$, and we also provide…

Algebraic Topology · Mathematics 2018-10-24 Daciberg L. Gonçalves , Thaís F. M. Monis , Stanisław Spież

The article presents a description of geometry of Banach structures forming mathematical base of markets arbitrage absence type phenomena. In this connection the role of reflexive subspaces (replacing classically considered…

Mathematical Finance · Quantitative Finance 2016-07-26 A. V. Lebedev , P. P. Zabreiko

We consider a class of inverse problems defined by a nonlinear map from parameter or model functions to the data. We assume that solutions exist. The space of model functions is a Banach space which is smooth and uniformly convex; however,…

Functional Analysis · Mathematics 2015-05-30 Maarten V. de Hoop , Lingyun Qiu , Otmar Scherzer

We introduce two notions of amenability for a Banach algebra $\cal A$. Let $I$ be a closed two-sided ideal in $\cal A$, we say $\cal A$ is $I$-weakly amenable if the first cohomology group of $\cal A$ with coefficients in the dual space…

Functional Analysis · Mathematics 2007-05-23 M E Gorgi , T Yazdanpanah

Manifold learning is a popular and quickly-growing subfield of machine learning based on the assumption that one's observed data lie on a low-dimensional manifold embedded in a higher-dimensional space. This thesis presents a mathematical…

Machine Learning · Computer Science 2020-11-04 Luke Melas-Kyriazi
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