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Maximum entropy feature fusion

: Baggenstoss, P.M.

Institute of Electrical and Electronics Engineers -IEEE-; International Society of Information Fusion -ISIF-; IEEE Aerospace and Electronic Systems Society -AESS-:
19th International Conference on Information Fusion, FUSION 2016. Proceedings : Heidelberg, 5-8 July 2016
Piscataway, NJ: IEEE, 2016
ISBN: 978-0-9964-5274-8
ISBN: 978-1-5090-2012-6
International Conference on Information Fusion (FUSION) <19, 2016, Heidelberg>
Fraunhofer FKIE ()

We review recent theoretical results in maximum entropy (MaxEnt) PDF projection that provide a theoretical framework for fusing the information from multiple features for the purpose of general statistical inference. Given a high-dimensional input data vector x, and several dimension-reducing feature transformations zi = Ti(x), we consider the problem of estimating the probability density function (PDF) of x by fusing the information in the various features. When the PDF of one feature p(zi) is known or has been estimated, the PDF pi(x) that has maximum entropy among all PDFs consistent with p(zi) can be constructed. This is called the maximum entropy projected PDFs and can serve as a generative models from which random samples can be drawn. The information from all the features can be fused into a common classifier structure either by testing each hypothesis with a different feature, or by combining the various projected PDFs in a mixture PDFs.