Zobrazeno 1 - 10
of 350
pro vyhledávání: '"Said, Salem"'
Autor:
Mostajeran, Cyrus, Said, Salem
The present work develops certain analytical tools required to construct and compute invariant kernels on the space of complex covariance matrices. The main result is the $\mathrm{L}^1\,-$Godement theorem, which states that any invariant kernel, whic
Externí odkaz:
http://arxiv.org/abs/2404.02169
This work aims to prove that the classical Gaussian kernel, when defined on a non-Euclidean symmetric space, is never positive-definite for any choice of parameter. To achieve this goal, the paper develops new geometric and analytical arguments. Thes
Externí odkaz:
http://arxiv.org/abs/2310.19270
Kernel methods are powerful tools in machine learning. Classical kernel methods are based on positive-definite kernels, which map data spaces into reproducing kernel Hilbert spaces (RKHS). For non-Euclidean data spaces, positive-definite kernels are
Externí odkaz:
http://arxiv.org/abs/2310.13821
This is an overview on the {source operator method} which leads to the construction of symmetry breaking differential operators (SBDO) in the context of tensor product of two principals series representations for the conformal group of a simple real
Externí odkaz:
http://arxiv.org/abs/2307.12141
Autor:
Said, Salem, Mostajeran, Cyrus
The integral of a function $f$ defined on a symmetric space $M \simeq G/K$ may be expressed in the form of a determinant (or Pfaffian), when $f$ is $K$-invariant and, in a certain sense, a tensor power of a positive function of a single variable. The
Externí odkaz:
http://arxiv.org/abs/2306.10390
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract A complete understanding of groundwater dynamics and its interaction with surface water under the impact of agricultural activities is vital for local agriculture, ecology, and residents of dry regions, which is not commonly recognized in ar
Externí odkaz:
https://doaj.org/article/57e3b1aab45d433b8ed688016eb56f97
Publikováno v:
Frontiers in Environmental Science, Vol 12 (2024)
Surface freshwater systems globally face severe stresses due to overpopulation and associated waste. The Ismailia Canal, a crucial freshwater source in the eastern Nile Delta, Egypt, serves multiple purposes and is endangered by various environmental
Externí odkaz:
https://doaj.org/article/63929d0ebf474d799d4279dc8525fa45
We present a novel algorithm for learning the parameters of hidden Markov models (HMMs) in a geometric setting where the observations take values in Riemannian manifolds. In particular, we elevate a recent second-order method of moments algorithm tha
Externí odkaz:
http://arxiv.org/abs/2207.00818
Riemannian Gaussian distributions were initially introduced as basic building blocks for learning models which aim to capture the intrinsic structure of statistical populations of positive-definite matrices (here called covariance matrices). While th
Externí odkaz:
http://arxiv.org/abs/2203.00204
Publikováno v:
In Heliyon 15 April 2024 10(7)