AN INVERSE VARIATIONAL CHARACTERIZATION OF THE KULLBACK–LEIBLER DIVERGENCE
DOI:
https://doi.org/10.51094/jxiv.2883キーワード:
Kullback–Leibler divergence、 Itakura–Saito divergence、 inverse problem、 variational、 characterization、 homogeneity、 marginal field、 logarithmic barrier、 exponential family、 information geometry抄録
We pose an inverse problem in information geometry: we identify minimal variational assumptions under which the Kullback–Leibler form is uniquely forced (up to scale), rather than postulated. Working in one dimension on the positive half-line R>0, we consider a C2 generating potential F ∶ R>0 → R in a dimensionless ratio coordinate ψ, normalized at the equilibrium ψ = 1. Our key postulate is imposed not on a statistical manifold or a divergence class but on the marginal field: we assume F'(ψ) ∈ span{1, ψ−1}, i.e., F' is a linear combination of the canonical 0- and (−1)-homogeneous types on R>0. Under strict convexity and equilibrium normalization, this determines the potential uniquely up to a positive constant, F (ψ) = c(ψ − 1 − ln ψ), c > 0, so the excess ψ − 1 − ln ψ is forced rather than selected from a broad class of divergences. We then record standard interpretations of this excess as the scalar Itakura–Saito divergence (a scale-invariant loss on R>0) in ratio form and as a concrete Kullback–Leibler divergence within a one-parameter exponential family, and briefly summarize the induced one-dimensional dually flat structure in the sense of information geometry.
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引用文献
S. M. Ali and S. D. Silvey, A general class of coefficients of divergence of one distribution from another, Journal of the Royal Statistical Society: Series B (Methodological) 28 (1966), no. 1, 131–142, DOI: https://doi.org/10.1111 j.2517-6161.1966.tb00626.x.
Shun-ichi Amari, Differential-geometrical methods in statistics, Lecture Notes in Statistics, vol. 28, Springer, 1985, DOI: https://doi.org/10.1007/978-1-4612-5056-2.
Shun-ichi Amari and Hiroshi Nagaoka, Methods of information geometry, Translations of Mathematical Monographs, vol. 191, American Mathematical Society and Oxford University Press, 2000, DOI: https://doi.org/10.1090/mmono/191.
L. M. Bregman, The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming, USSR Computational Mathematics and Mathematical Physics 7 (1967), no. 3, 200–217, DOI: https://doi.org/10.1016/0041-5553(67)90040-7.
Thomas M. Cover and Joy A. Thomas, Elements of information theory, 2 ed., Wiley, 2006, DOI (online book record): https://doi.org/10.1002/047174882X.
Imre Csiszár, Information-type measures of difference of probability distributions and indirect observations, Studia Scientiarum Mathematicarum Hungarica 2 (1967), 299–318, No DOI found; metadata: https://cir.nii.ac.jp/crid/1571417125811646464.
Shinto Eguchi, Geometry of minimum contrast, Hiroshima Mathematical Journal 22 (1992), no. 3, 631–647, DOI: https://doi.org/10.32917/hmj/1206128508.
Cédric Févotte, Nancy Bertin, and Jean-Louis Durrieu, Nonnegative matrix factorization with the Itakura–Saito divergence: With application to music analysis, Neural Computation 21 (2009), no. 3, 793–830, DOI: https://doi.org/10.1162/neco.2008.04-08-771.
Takeo Imaizumi, Optimal entropic dimensionality: A continuous variational principle for geometric equilibrium, Jxiv, JST Preprint Server, December 2025, Version 1. DOI: https://doi.org/10.51094/jxiv.2161.
Takeo Imaizumi, A geometric representation of the second law in optimal entropic dimensionality, Jxiv preprint, 2026, Jxiv (physics), version 1. DOI: https://doi.org/10.51094/jxiv.2601.
Fumitada Itakura and Shuzo Saito, Analysis synthesis telephony based on the maximum likelihood method, Proceedings of the 6th International Congress on Acoustics (Tokyo, Japan), 1968, No DOI found; metadata: https://cir.nii.ac.jp/crid 1570854175842518528, pp. C–17–C–20.
Richard Jordan, David Kinderlehrer, and Felix Otto, The variational formulation of the fokker–planck equation, SIAM Journal on Mathematical Analysis 29 (1998), no. 1, 1–17, DOI: https://doi.org/10.1137/S0036141096303359.
Solomon Kullback and Richard A. Leibler, On information and sufficiency, The Annals of Mathematical Statistics 22 (1951), no. 1, 79–86, DOI: https://doi.org/10.1214/aoms/1177729694.
Cédric Villani, Optimal transport: Old and new, Grundlehren der mathematischen Wissenschaften, vol. 338, Springer, 2009, DOI: https://doi.org/10.1007/978-3-540-71050-9.
Martin J. Wainwright and Michael I. Jordan, Graphical models, exponential families, and variational inference, vol. 1, Foundations and Trends in Machine Learning, no. 1–2, Now Publishers, 2008, DOI: https://doi.org/10.1561/2200000001.
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公開日時: 2026-03-04 09:38:56 UTC
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