An ellipsoid constrained quadratic programming (ECQP) approach to MCE training of MQDF-based classifiers for handwriting recognition

In this study, we propose a novel optimization algorithm for minimum classification error (MCE) training of modified quadratic discriminant function (MQDF) models. An ellipsoid constrained quadratic programming (ECQP) problem is formulated with an efficient line search solution derived, and a subspace combination condition is proposed to simplify the problem in certain cases. We show that under the perspective of constrained optimization, the MCE training of MQDF models can be solved by ECQP with some reasonable approximation, and the hurdle of incomplete covariances can be handled by subspace combination. Experimental results on the Nakayosi/Kuchibue online handwritten Kanji character recognition task show that compared with the conventional generalized probabilistic descent (GPD) algorithm, the new approach achieves about 7% relative error rate reduction.