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Journal ArticleDOI

Information geometry of divergence functions

Shun-ichi Amari, +1 more
- 01 Mar 2010 - 
- Vol. 58, Iss: 1, pp 183-195
TLDR
This article studies the differential-geometrical structure of a manifold induced by a divergence function, which consists of a Riemannian metric, and a pair of dually coupled affine connections, which are studied in information geometry.
Abstract
Measures of divergence between two points play a key role in many engineering problems. One such measure is a distance function, but there are many important measures which do not satisfy the properties of the distance. The Bregman divergence, KullbackLeibler divergence and f -divergence are such measures. In the present article, we study the differential-geometrical structure of a manifold induced by a divergence function. It consists of a Riemannian metric, and a pair of dually coupled affine connections, which are studied in information geometry. The class of Bregman divergences are characterized by a dually flat structure, which is originated from the Legendre duality. A dually flat space admits a generalized Pythagorean theorem. The class of f -divergences, defined on a manifold of probability distributions, is characterized by information monotonicity, and the Kullback-Leibler divergence belongs to the intersection of both classes. The f -divergence always gives the α-geometry, which consists of the Fisher information metric and a dual pair of ±α-connections. The α-divergence is a special class of f -divergences. This is unique, sitting at the intersection of the f -divergence and Bregman divergence classes in a manifold of positive measures. The geometry derived from the Tsallis q-entropy and related divergences are also addressed.

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Citations
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Journal ArticleDOI

Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities

Andrzej Cichocki, +1 more
- 14 Jun 2010 - 
TL;DR: It is shown that a new wide class of Gamma-divergences can be generated not only from the family of Beta-diversgences but also from a family of Alpha-d divergences.
Journal ArticleDOI

Generalized Alpha-Beta Divergences and Their Application to Robust Nonnegative Matrix Factorization

TL;DR: Owing to more degrees of freedom in tuning the parameters, the proposed family of AB-multiplicative NMF algorithms is shown to improve robustness with respect to noise and outliers.
Journal ArticleDOI

$\alpha$ -Divergence Is Unique, Belonging to Both $f$ -Divergence and Bregman Divergence Classes

TL;DR: It is proved that the alpha-divergences constitute a unique class belonging to both classes when the space of positive measures or positive arrays is considered, and this is the only such one in thespace of probability distributions.
Journal ArticleDOI

Divergence-Based Framework for Common Spatial Patterns Algorithms

TL;DR: It is shown that the popular common spatial patterns (CSP) algorithm can be formulated as a divergence maximization problem and computed within this framework and unifies many of the recently proposed CSP variants in a principled manner.
Journal ArticleDOI

Riemannian Medians and Means With Applications to Radar Signal Processing

TL;DR: This paper introduces radar data and the problem of target detection, and shows how to transform the original radar data into Toeplitz covariance matrices, and proposes deterministic and stochastic algorithms to compute p-means.
References
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