Feature Selection with Non-Parametric Mutual Information for Adaboost Learning

This paper describes a feature selection method based on the quadratic mutual information. We describe the needed formulation to estimate the mutual information from the data. This paper is motivated for the high time cost of the training process using the classical boosting algorithms. This method allows to reuse part of the training time used in the first training process to speed up posterior training to update the detectors in front of samples changes.