Feature extraction based on the Bhattacharyya distance

Abstract

The authors propose a feature extraction method based on the Bhattacharyya distance. Recently, it has been reported that an accurate estimation of classification error is possible using the Bhattacharyya distance. In the proposed method, the authors try to find feature vectors that minimize the estimated classification error of Gaussian ML classifier. In order to find such feature vectors, they start with arbitrary initial feature vectors and update them using two optimization techniques: sequential search and global search. Since they use the error estimation equation for updating feature vectors, the search time can be reduced significantly. They first apply the algorithm to two class problems and extend it to multiclass problems. Experimental results show that the proposed feature extraction algorithm compares favorably with conventional feature extraction algorithms.