Feature evaluation using quadratic mutual information
Dongyang Xu, José Carlos Príncipe · 2002
Methods of feature evaluation are developed and discussed based on information theoretical learning (ITL). Mutual information was shown in the literature to be more robust and precise to evaluate a feature set. We propose to use quadratic mutual information (QMI) for feature evaluation. The concept of information potential leads to a more clearly physical meaning of the evaluation functions. Moreover, evaluation for feature sets in high-dimensional space could also be implemented efficiently. Experimental results are compared to classifier performances.