Regularized margin-based conditional log-likelihood loss for prototype learning

Abstract

The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithm. The minimum classification error (MCE) method and the soft nearest prototype classifier (SNPC) method are two important algorithms using misclassification loss. This paper proposes a new prototype learning algorithm based on the conditional log-likelihood loss (CLL), which is based on the discriminative model called log-likelihood of margin (LOGM). A regularization term is added to avoid over-fitting in training as well as to maximize the hypothesis margin. The CLL in the LOGM algorithm is a convex function of margin, and so, shows better convergence than the MCE. In addition, we show the effects of distance metric learning with both prototype-dependent weighting and prototype-independent weighting. Our empirical study on the benchmark datasets demonstrates that the LOGM algorithm yields higher classification accuracies than the MCE, generalized learning vector quantization (GLVQ), soft nearest prototype classifier (SNPC) and the robust soft learning vector quantization (RSLVQ), and moreover, the LOGM with prototype-dependent weighting achieves comparable accuracies to the support vector machine (SVM) classifier.