Locally Smoothed Modified Quadratic Discriminant Function

Modified quadratic discriminant function (MQDF) is a state-of-the-art classifier for handwriting recognition. However, the big gap between accuracies on training and testing sets indicates that MQDF has a good capability to fit training data but the generalization performance is not promising. To solve this problem, we propose a new model called locally smoothed modified quadratic discriminant function (LSMQDF) by smoothing the covariance matrix of each class with its nearest neighbor classes. LSMQDF can be viewed as a regularization to avoid over-fitting. The covariance matrix estimated by local smoothing is more accurate and robust. LSMQDF can be also viewed as an extension of the global smoothing method, namely regularized discriminant analysis (RDA). Experiments on both offline and online Chinese handwriting databases demonstrate that: with local smoothing, the accuracy on training set is decreased (over-fitting avoided), and the accuracy on testing set is improved significantly and consistently (generalization improved).