On Coarse Graining of Information and Its Application to Pattern Recognition
This work addresses pattern recognition tasks, but it appears incremental as it builds on existing mixture model and maximum entropy techniques without clear broad impact.
The authors tackled the problem of classifying observations into categories by proposing a method based on finite mixture models, using maximum entropy and Pythagorean means to derive component densities, with examples provided for distributions in the Pythagorean family.
We propose a method based on finite mixture models for classifying a set of observations into number of different categories. In order to demonstrate the method, we show how the component densities for the mixture model can be derived by using the maximum entropy method in conjunction with conservation of Pythagorean means. Several examples of distributions belonging to the Pythagorean family are derived. A discussion on estimation of model parameters and the number of categories is also given.