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The existing research on spectral algorithms, applied within a Reproducing Kernel Hilbert Space (RKHS), has primarily focused on general kernel functions, often neglecting the inherent structure of the input feature space. Our paper…

Machine Learning · Statistics 2024-03-08 Weichun Xia , Lei Shi

The embedding theorem arises in several problems from analysis and geometry. The purpose of this paper is to provide a deeper understanding of analysis and geometry with a particular focus on embedding theorems on spaces of homogeneous type…

Classical Analysis and ODEs · Mathematics 2016-01-25 Yanchang Han , Yongsheng Han , Ji Li

We prove an equivariant implicit function theorem for variational problems that are invariant under a varying symmetry group (corresponding to a bundle of Lie groups). Motivated by applications to families of geometric variational problems…

Differential Geometry · Mathematics 2014-12-02 Renato G. Bettiol , Paolo Piccione , Gaetano Siciliano

We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-linear way. Each node is encoded with a probability density…

Machine Learning · Computer Science 2020-02-07 Thorben Funke , Tian Guo , Alen Lancic , Nino Antulov-Fantulin

We show that for smooth manifolds X and Y, any isomorphism between the special algebra of Colombeau generalized functions on X, resp. Y is given by composition with a unique Colombeau generalized function from Y to X. We also identify the…

Functional Analysis · Mathematics 2017-05-24 Hans Vernaeve

In this work we introduce a new algebra of tempered generalized functions. The tempered distributions are embedded in this algebra via their Hermite expansions. The Fourier transform is naturally extended to this algebra in such a way that…

Functional Analysis · Mathematics 2010-07-01 P. Catuogno , C. Olivera

The study of embeddings of smooth manifolds into Euclidean and projective spaces has been for a long time an important area in topology. In this paper we obtain improvements of classical results on embeddings of smooth manifolds, focusing…

Algebraic Topology · Mathematics 2015-06-16 Victor Buchstaber , Andrey Kustarev

Potential functions can be used as generating potentials of relevant geometric structures for a Riemannian manifold such as the Riemannian metric and affine connections. We study wether this procedure can also be applied to tensors of rank…

Differential Geometry · Mathematics 2019-02-04 Florio M. Ciaglia , Giuseppe Marmo , Juan Manuel Pérez-Pardo

A generalized Stiefel manifold is the manifold of orthonormal frames in a vector space with a non-degenerated bilinear or hermitian form. In this article, the Isometry group of the generalized Stiefel manifolds are computed at least up to…

Differential Geometry · Mathematics 2019-05-22 Manuel Sedano-Mendoza

Nontrivial isometric embeddings for flat metrics (i.e., those which are not just planes in the ambient space) can serve as useful tools in the description of gravity in the embedding gravity approach. Such embeddings can additionally be…

General Relativity and Quantum Cosmology · Physics 2021-12-21 S. A. Paston , T. I. Zaitseva

We construct spectral triples in a sense of noncommutative differential geometry, associated with a Riemannian foliation on a compact manifold, and describe its dimension spectrum.

dg-ga · Mathematics 2008-02-03 Yuri A. Kordyukov

Computing the embedding distribution of a given graph is a fundamental question in topological graph theory. In this article, we extend our viewpoint to a sequence of graphs and consider their asymptotic embedding distributions, which are…

Combinatorics · Mathematics 2025-07-22 Yichao Chen , Wenjie Fang , Zhicheng Gao , Jinlian Zhang

Pulling back complex structures along a branched covering induces a holomorphic isometric embedding of Teichm\"uller spaces. We show that for dimension at least $2$, all isometric embeddings arise from branched coverings. This generalizes a…

Geometric Topology · Mathematics 2023-05-09 Frederik Benirschke , Carlos A. Serván

On a smooth manifold with distributions ${\cal D}_1$ and ${\cal D}_2$ having trivial intersection, we consider the integral of their mutual curvature, as a functional of Riemannian metrics that make the distributions orthogonal. The mutual…

Differential Geometry · Mathematics 2023-03-07 Vladimir Rovenski , Tomasz Zawadzki

By introducing the notion of distributive constant for a family of closed subschemes, we establish a general form of the second main theorem for algebraic nondegenerate meromorphic mappings from a generalized $p$-Parabolic manifold into a…

Complex Variables · Mathematics 2026-02-17 Si Duc Quang

The scattering transform is a multilayered, wavelet-based transform initially introduced as a model of convolutional neural networks (CNNs) that has played a foundational role in our understanding of these networks' stability and invariance…

An algorithm for embedding finite dimensional Lie algebras into Lie algebras of vector fields (and Lie superalgebras into Lie superalgebras of vector fields) is offered in a way applicable over ground fields of any characteristic. The…

Representation Theory · Mathematics 2009-11-11 Irina Shchepochkina

We introduce $(k,l)$-regular maps, which generalize two previously studied classes of maps: affinely $k$-regular maps and totally skew embeddings. We exhibit some explicit examples and obtain bounds on the least dimension of a Euclidean…

Differential Geometry · Mathematics 2007-05-23 Gordana Stojanovic

We construct a diffeomorphism invariant (Colombeau-type) differential algebra canonically containing the space of distributions in the sense of L. Schwartz. Employing differential calculus in infinite dimensional (convenient) vector spaces,…

Functional Analysis · Mathematics 2007-05-23 Eva Farkas , Michael Grosser , Michael Kunzinger , Roland Steinbauer

Representing graphs as sets of node embeddings in certain curved Riemannian manifolds has recently gained momentum in machine learning due to their desirable geometric inductive biases, e.g., hierarchical structures benefit from hyperbolic…

Machine Learning · Computer Science 2020-06-09 Calin Cruceru , Gary Bécigneul , Octavian-Eugen Ganea
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