A New Conic Approach to Semisupervised Support Vector Machines
We propose a completely positive programming reformulation of the 2 norm soft margin model. Then, we construct a sequence of computable cones of nonnegative quadratic forms over a union of second order cones to approximate the underlying completely positive cone. An optimal solution can be found in finite iterations using semidefinite programming techniques by our method. Moreover, in order to obtain a good lower bound efficiently, an adaptive scheme is adopted in our approximation algorithm. The numerical results show that the proposed algorithm can achieve more accurate classifications than other well known conic relaxations of semisupervised support vector machine models in the literature.