Information Geometry and Minimum Description Length Networks

author: Ke Sun, University of Geneva
published: Sept. 27, 2015,   recorded: July 2015,   views: 1502

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We study parametric unsupervised mixture learning. We measure the loss of intrinsic information from the observations to complex mixture models, and then to simple mixture models. We present a geometric picture, where all these representations are regarded as free points in the space of probability distributions. Based on minimum description length, we derive a simple geometric principle to learn all these models together. We present a new learning machine with theories, algorithms, and simulations.

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