New bilinear formulation to semi-supervised classification based on Kernel Spectral Clustering

In this paper we present a novel semi-supervised classification approach which combines bilinear formulation for non-parallel binary classifiers based upon Kernel Spectral Clustering. The cornerstone of our approach is a bilinear term introduced into the primal formulation of semi-supervised classification problem. In addition we perform separate manifold regularization for each individual classifier. The latter relates to the Kernel Spectral Clustering unsupervised counterpart which helps to obtain more precise and generalizable classification boundaries. We derive the dual problem which can be effectively translated into a linear system of equations and then solved without introducing extra costs. In our experiments we show the usefulness and report considerable improvements in performance with respect to other semi-supervised approaches, like Laplacian SVMs and other KSC-based models.