Unsupervised and Semi-Supervised Two-class Support Vector Machines
Kun Zhao, Tian Ying-jie, Deng Nai-yang · 2006
Support vector machines have been a dominant learning technique for almost ten years, moreover they have been applied to supervised learning problems. Recently two-class unsupervised and semi-supervised classification problems based on bounded c-support vector machines are relaxed to semi-definite programming (B.L. Xu et al., 2004). In this paper the authors present another version to two-class unsupervised and semi-supervised classification problems based on bounded v-support vector machines, which trained by convex relaxation of the training criterion: find a labeling that yield a maximum margin on the training data. But the problems have difficulty to compute, we will find their semi-definite relaxations that can approximate them well. Experimental results show that our new unsupervised and semi-supervised classification algorithms often obtain more accurate results than other unsupervised and semi-supervised methods