Semi-Supervised Learning Using Semi-Definite Programming
de Bie Tijl, Nello Cristianini · The MIT Press eBooks · 2006
This chapter discusses an alternative approach that is based on a convex relaxation of the optimization problem associated with support vector machine transduction. The result is a semi-definite programming (SDP) problem which can be optimized in polynomial time, the solution of which is an approximation of the optimal labeling as well as a bound on the true optimum of the original transduction objective function. To further decrease the computational complexity, this chapter proposes an approximation that allows solving transduction problems of up to 1,000 unlabeled samples. Finally, the formulation is extended to more general settings of semi-supervised learning, where equivalence and inequivalence constraints are given on labels of some of the samples.