Feature extraction based on minimum classification error/generalized probabilistic descent method

A novel approach to pattern recognition which comprehensively optimizes both a feature extraction process and a classification process is introduced. Assuming that the best features for recognition are the ones that yield the lowest classification error rate over unknown data, an overall recognizer, consisting of a feature extractor module and a classifier module, is trained using the minimum classification error (MCE)/generalized probabilistic descent (GPD) method. Although the proposed discriminative feature extraction approach is a direct and simple extension of MCE/GPD, it is a significant departure from conventional approaches, providing a comprehensive basis for the entire system design. Experimental results are presented for the simple example of optimally designing a cepstrum representation for vowel recognition. The results clearly demonstrate the effectiveness of the proposed method.<>