Prototype learning with margin-based conditional log-likelihood loss

Abstract

The classification performance of nearest prototype classifiers largely relies on the prototype learning algorithms, such as the learning vector quantization (LVQ) and the minimum classification error (MCE). This paper proposes a new prototype learning algorithm based on the minimization of a conditional log-likelihood loss (CLL), called log-likelihood of margin (LOGM). A regularization term is added to avoid over-fitting in training. The CLL loss in LOGM is a convex function of margin, and so, gives better convergence than the MCE algorithm. Our empirical study on a large suite of benchmark datasets demonstrates that the proposed algorithm yields higher accuracies than the MCE, the generalized LVQ (GLVQ), and the soft nearest prototype classifier (SNPC).