Results 11 to 20 of about 3,294 (168)

Understanding Higher-Order Interactions in Information Space [PDF]

Entropy
Methods used in topological data analysis naturally capture higher-order interactions in point cloud data embedded in a metric space. This methodology was recently extended to data living in an information space, by which we mean a space measured with an
Herbert Edelsbrunner, Katharina Ölsböck, Hubert Wagner   +2 more
doaj   +2 more sources

Mirror Descent and Exponentiated Gradient Algorithms Using Trace-Form Entropies [PDF]

Entropy
This paper introduces a broad class of Mirror Descent (MD) and Generalized Exponentiated Gradient (GEG) algorithms derived from trace-form entropies defined via deformed logarithms. Leveraging these generalized entropies yields MD and GEG algorithms with
Andrzej Cichocki, Toshihisa Tanaka, Frank Nielsen, Sergio Cruces   +3 more
doaj   +2 more sources

Generalized Legendre Transforms Have Roots in Information Geometry [PDF]

Entropy
Artstein-Avidan and Milman [Annals of mathematics (2009), (169):661–674] characterized invertible reverse-ordering transforms in the space of lower, semi-continuous, extended, real-valued convex functions as affine deformations of the ordinary Legendre ...
Frank Nielsen
doaj   +2 more sources

Information Geometry for Radar Target Detection with Total Jensen–Bregman Divergence [PDF]

Entropy, 2018
This paper proposes a radar target detection algorithm based on information geometry. In particular, the correlation of sample data is modeled as a Hermitian positive-definite (HPD) matrix. Moreover, a class of total Jensen–Bregman divergences, including
Xiaoqiang Hua, Haiyan Fan, Yongqiang Cheng, Hongqiang Wang, Yuliang Qin   +4 more
doaj   +2 more sources

Learning to Approximate a Bregman Divergence [PDF]

, 2020
Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergence, and arise throughout many areas of machine learning.
Castanon, David, Kulis, Brian, Saligrama, Venkatesh, Siahkamari, Ali, Xia, Xide   +4 more
core   +2 more sources

Centroid-Based Clustering with ab-Divergences [PDF]

Entropy (Basel), 2019
Centroid-based clustering is a widely used technique within unsupervised learning algorithms in many research fields. The success of any centroid-based clustering relies on the choice of the similarity measure under use.
Cruces Álvarez, Sergio Antonio, Durán Díaz, Iván, Fondón García, Irene, Sarmiento Vega, María Auxiliadora   +3 more
core   +3 more sources

Hyperlink regression via Bregman divergence [PDF]

Neural Networks, 2020
41 pages, 14 ...
Akifumi Okuno, Hidetoshi Shimodaira
openaire   +3 more sources

Maximizing the Bregman divergence from a Bregman family [PDF]

Kybernetika, 2020
11 pages, 5 theorems, no ...
Rauh, Johannes, Matúš, František
openaire   +4 more sources

Bregman divergences for physically informed discrepancy measures for learning and computation in thermomechanics

Comptes Rendus. Mécanique, 2023
With view on the context of convex thermomechanics, we propose tools based on the concept of Bregman divergence, a notion introduced in the 1960s and used in learning and optimization as well. This study is motivated by the need of “discrepancy measures”
Andrieux, Stéphane
doaj   +1 more source

Transport information Bregman divergences [PDF]

Information Geometry, 2021
Typos are ...
openaire   +3 more sources