Semi-supervised learning with mixed knowledge information

Integrating new knowledge sources into various learning tasks to improve their performance has recently become an interesting topic. In this paper we propose a novel semi-supervised learning (SSL) approach, called semi-supervised learning with Mixed Knowledge Information (SSL-MKI) which can simultaneously handle both sparse labeled data and additional pairwise constraints together with unlabeled data. Specifically, we first construct a unified SSL framework to combine the manifold assumption and the pairwise constraints assumption for classification tasks. Then we present a Modified Fixed Point Continuation (MFPC) algorithm with an eigenvalue thresholding (EVT) operator to learn the enhanced kernel matrix. Finally, we develop a two-stage optimization strategy and provide an efficient SSL approach that takes advantage of Laplacian spectral regularization: semi-supervised learning with Enhanced Spectral Kernel (ESK). Experimental results on a variety of synthetic and real-world datasets demonstrate the effectiveness of the proposed ESK approach