A Smoothing Optimization Approach Applied to the Supervised MDS Method

This paper presents an efficient approach to the Supervised MDS method. This method handles the problems of data visualization, supervised classification and bipartite ranking. In order to overcome the non-differentiable nature of the Supervised MDS method, the mathematical formulation proposed in this work is based on the hyperbolic smoothing technique. The performance of the algorithm is evaluated by computational experiments. The results show that the proposed methodology presented, in most cases, better results than the results available in the literature. Furthermore, the methodology presents a good performance in relation to the methods Logistic regression, Naive Bayes and Support Vector Machine.