Similarity Learning Based on Multiple Support Vector Data Description

Similarity learning ranges over an extensive field in machine learning and pattern recognition. This paper deals with similarity learning based on multiple support vector data description (SVDD). It is well known that SVDD was proposed for one-class or two-class unbalanced learning problems. Thus, we propose a multiple SVDD (MSVDD) algorithm and apply it to multi-class learning problems. A SVDD model is trained by similar pairwise samples in the same class instead of all similar ones. In addition, the dissimilar pairwise samples are not considered in MSVDD. Experimental results validate that MSVDD is promising in similarity learning.