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Title

Information Geometric Density Estimation

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Published in Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Clos Lucé, Amboise, France - 21–26 September 2014 - . 2014, p. 222-229
Collection AIP Conference Proceedings; 1641
Abstract 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.
Keywords Information geometryDensity estimation
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Research groups Viper group
Computer Vision and Multimedia Laboratory
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SUN, Ke, MARCHAND-MAILLET, Stéphane. Information Geometric Density Estimation. In: Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Clos Lucé, Amboise, France. [s.l.] : [s.n.], 2014. p. 222-229. (AIP Conference Proceedings; 1641) https://archive-ouverte.unige.ch/unige:73191

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Deposited on : 2015-06-15

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