Results 11 to 20 of about 7,890 (182)

The Bregman Chord Divergence [PDF]

, 2019
Distances are fundamental primitives whose choice significantly impacts the performances of algorithms in machine learning and signal processing. However selecting the most appropriate distance for a given task is an endeavor. Instead of testing one by one the entries of an ever-expanding dictionary of {\em ad hoc} distances, one rather prefers to ...
Frank Nielsen, Richard Nock
openaire   +3 more sources

Learning to Approximate a Bregman Divergence [PDF]

CoRR, 2019
19 pages, 4 ...
Ali Siahkamari, Xide Xia, Venkatesh Saligrama, David A. Castañón, Brian Kulis   +4 more
core   +5 more sources

A Characterization of the Domain of Beta-Divergence and Its Connection to Bregman Variational Model

Entropy, 2017
In image and signal processing, the beta-divergence is well known as a similarity measure between two positive objects. However, it is unclear whether or not the distance-like structure of beta-divergence is preserved, if we extend the domain of the beta-
Hyenkyun Woo
exaly   +3 more sources

On Minimum Bregman Divergence Inference

Mathematics
The density power divergence (DPD) is a well-studied member of the Bregman divergence family and forms the basis of widely used minimum divergence estimators that balance efficiency and robustness. In this paper, we introduce and study a new sub-class of
Soumik Purkayastha, Ayanendranath Basu
doaj   +3 more sources

Divergence and Sufficiency for Convex Optimization

Entropy, 2017
Logarithmic score and information divergence appear in information theory, statistics, statistical mechanics, and portfolio theory. We demonstrate that all these topics involve some kind of optimization that leads directly to regret functions and such ...
Peter Harremoës
doaj   +5 more sources

Block-Active ADMM to Minimize NMF with Bregman Divergences [PDF]

Sensors, 2023
Over the last ten years, there has been a significant interest in employing nonnegative matrix factorization (NMF) to reduce dimensionality to enable a more efficient clustering analysis in machine learning.
Xinyao Li, Akhilesh Tyagi
doaj   +2 more sources

Update of Prior Probabilities by Minimal Divergence [PDF]

Entropy, 2021
The present paper investigates the update of an empirical probability distribution with the results of a new set of observations. The update reproduces the new observations and interpolates using prior information.
Jan Naudts
doaj   +2 more sources

Anomaly Detection in High-Dimensional Time Series Data with Scaled Bregman Divergence [PDF]

Algorithms
The purpose of anomaly detection is to identify special data points or patterns that significantly deviate from the expected or typical behavior of the majority of the data, and it has a wide range of applications across various domains.
Yunge Wang, Lingling Zhang, Tong Si, Graham Bishop, Haijun Gong   +4 more
doaj   +2 more sources

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

Revisiting Chernoff Information with Likelihood Ratio Exponential Families [PDF]

Entropy, 2022
The Chernoff information between two probability measures is a statistical divergence measuring their deviation defined as their maximally skewed Bhattacharyya distance.
Frank Nielsen
doaj   +2 more sources