An improved feature transformation method using mutual information

Abstract

The feature transformation is a very important step in pattern recognition systems. A feature transformation matrix can be obtained using different criteria such as discrimination between classes or feature independence or mutual information between features and classes. The obtained matrix can also be used for feature reduction. In this paper, we propose a new method for finding a feature transformation-based on Mutual Information (MI). For this purpose, we suppose that the Probability Density Function (PDF) of features in classes is Gaussian, and then we use the gradient ascent to maximize the mutual information between features and classes. Experimental results show that the proposed MI projection consistently outperforms other methods for a variety of cases. In the UCI Glass database we improve the classification accuracy up to 7.95 %. Besides, the improvement of phoneme recognition rate is 3.55 % on TIMIT.