Mathematical structure of Information Distortion methods Tomas Gedeon, Department of Mathematics, Montana State University
In this talk we introduce Information Distortion function used in
neural decoding problem and compare it to a closely related function used in
Information Bottleneck. We discuss the annealing approach to finding
an optimal solution for both functions. We show that there is a close
relationship between the first phase transition in the annealing and the
Approximate Normalized Cut for certain graph G.
We also show that we can explicitely compute values of the annealing parameter
where the phase transitions happen. As a consequence the annealing process for
both functions can be characterized as Deterministic annealing.