Graphical Lasso Quadratic Discriminant Function for Character Recognition

The quadratic discriminant function (QDF) derived from the multivariate Gaussian distribution is effective for classification in many pattern recognition tasks. In particular, a variant of QDF, called MQDF, has achieved great success and is widely recognized as the state-of-the-art method in character recognition. However, when the number of training samples is small, covariance estimation involved in QDF will usually be ill-posed, and it leads to the loss of the classification accuracy. To attack this problem, in this paper, we engage the graphical lasso method to estimate the covariance and propose a new classification method called the Graphical Lasso Quadratic Discriminant Function (GLQDF). By exploiting a coordinate descent procedure for the lasso, GLQDF can estimate the covariance matrix (and its inverse) more precisely. Experimental results demonstrate that the proposed method can perform better than the competitive methods on two artificial and six real data sets (including both benchmark digit and Chinese character data).