Bregman divergence - Open Access .click

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Coherent Forecasting of Realized Volatility

Journal of Forecasting, EarlyView.
ABSTRACT The QLIKE loss function is the stylized favorite of the literature on volatility forecasting when it comes to out‐of‐sample evaluation and the state of the art model for realized volatility (RV) forecasting is the HAR model, which minimizes the squared error loss for in‐sample estimation of the parameters.
Marius Puke, Karsten Schweikert
wiley +1 more source

Non-Extensive Entropic Distance Based on Diffusion: Restrictions on Parameters in Entropy Formulae

Entropy, 2016
Based on a diffusion-like master equation we propose a formula using the Bregman divergence for measuring entropic distance in terms of different non-extensive entropy expressions. We obtain the non-extensivity parameter range for a universal approach to
Tamás Sándor Biró, Zsolt Schram
doaj +1 more source

Genome‐Wide Assessment Reveals Ancestral Differences in Homozygosity Patterns Potentially Linked to Parkinson's Disease Etiology

Movement Disorders, EarlyView.
Abstract Background Recessive genetic variation and extended runs of homozygosity (ROHs) may contribute to the unexplained heritability of Parkinson's disease (PD), particularly in diverse and understudied populations. Objective We conducted the first large‐scale, multi‐ancestral investigation of PD to examine the impact of genome‐wide homozygosity on ...
Kathryn Step +680 more
wiley +1 more source

Information Geometry of Positive Measures and Positive-Definite Matrices: Decomposable Dually Flat Structure

Entropy, 2014
Information geometry studies the dually flat structure of a manifold, highlighted by the generalized Pythagorean theorem. The present paper studies a class of Bregman divergences called the (ρ,τ)-divergence.
Shun-ichi Amari
doaj +1 more source

Proper scoring rules and Bregman divergence

Bernoulli, 2018
We revisit the mathematical foundations of proper scoring rules (PSRs) and Bregman divergences and present their characteristic properties in a unified theoretical framework. In many situations it is preferable not to generate a PSR directly from its convex entropy on the unit simplex but instead by the sublinear extension of the entropy to the ...
openaire +3 more sources

Matrix Nearness Problems with Bregman Divergences [PDF]

SIAM Journal on Matrix Analysis and Applications, 2008
This paper discusses a new class of matrix nearness problems that measure approximation error using a directed distance measure called a Bregman divergence. Bregman divergences offer an important generalization of the squared Frobenius norm and relative entropy, and they all share fundamental geometric properties.
Dhillon, Inderjit S., Tropp, Joel A.
openaire +2 more sources

Existence Analysis of a Three‐Species Memristor Drift‐Diffusion System Coupled to Electric Networks

Mathematical Methods in the Applied Sciences, EarlyView.
ABSTRACT The existence of global weak solutions to a partial‐differential‐algebraic system is proved. The system consists of the drift‐diffusion equations for the electron, hole, and oxide vacancy densities in a memristor device, the Poisson equation for the electric potential, and the differential‐algebraic equations for an electric network.
Ansgar Jüngel, Tuấn Tùng Nguyến
wiley +1 more source

Worst-case and smoothed analysis of k-means clustering with Bregman divergences

Journal of Computational Geometry, 2013
The k-means method is the method of choice for clustering large-scale data sets and it performs exceedingly well in practice despite its exponential worst-case running-time.
Bodo Manthey, Heiko Roeglin
doaj +1 more source

A Spatio-Temporal Co-Clustering Framework for Discovering Mobility Patterns: A Study of Manhattan Taxi Data

IEEE Access, 2021
Research on clustering spatio-temporal data to extract mobility patterns requires further development, as most existing studies do not simultaneously integrate data along both spatial dimensions and temporal dimensions but instead focus on only one ...
Qian Liu +4 more
doaj +1 more source

Neural Bregman Divergences for Distance Learning

, 2022
Many metric learning tasks, such as triplet learning, nearest neighbor retrieval, and visualization, are treated primarily as embedding tasks where the ultimate metric is some variant of the Euclidean distance (e.g., cosine or Mahalanobis), and the algorithm must learn to embed points into the pre-chosen space.
Lu, Fred, Raff, Edward, Ferraro, Francis
openaire +2 more sources

fos: computer and information sciences
machine learning cs.lg
bregman divergences

information geometry
computer science - machine learning
convexity

fos: mathematics
information theory cs.it
relative entropy