Maximum entropy feature fusion

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 z
_i
= T
_i
(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(z
_i
) is known or has been estimated, the PDF p
_i
(x) that has maximum entropy among all PDFs consistent with p(z
_i
) 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. We review related theoretical and experimental results and provide a simulated classification experiment to highlight the potential of the method.