Information geometric density estimation | Zendy

Ke Sun | Zendy; Stéphane MarchandMaillet | Zendy

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Information geometric density estimation

Author(s) -

Ke Sun,

Stéphane MarchandMaillet

Publication year - 2015

Publication title -

aip conference proceedings

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.177

H-Index - 75

eISSN - 1551-7616

pISSN - 0094-243X

DOI - 10.1063/1.4905982

Subject(s) - density estimation , kernel density estimation , estimator , multivariate kernel density estimation , computer science , probability density function , variable kernel density estimation , information geometry , point process , kernel (algebra) , statistical manifold , artificial intelligence , mathematics , kernel method , mathematical optimization , algorithm , statistics , geometry , support vector machine , discrete mathematics , scalar curvature , curvature

We investigate kernel density estimation where the kernel function varies from point to point. Density estimation in the input space means to find a set of coordinates on a statistical manifold. This novel perspective helps to combine efforts from information geometry and machine learning to spawn a family of density estimators. We present example models with simulations. We discuss the principle and theory of such density estimation

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