Spectral Energy Minimization for Semi-supervised Learning

The use of unlabeled data to aid classification is important as labeled data is often available in limited quantity. Instead of utilizing training samples directly into semi-supervised learning, energy function incorporating the conditional probability of classification is adopted. The semi-supervised learning is posed as the optimization of both the classification energy and the cluster compactness energy. The resulting integer programming is relaxed by a semi-definite relaxation where efficient solution can be obtained. Furthermore, the spectral graph methods provide improved energy minimization via the incorporation of additional criteria. Results on UCI datasets show promising results. Spectral Energy Minimization for Semi-supervised Learning Chun-hung Li, Zhi Li Wu Department of Computer Science Hong Kong Baptist University Abstract The use of unlabeled data to aid classification is important as labeled data is often available in limited quantity. Instead of utilizing training samples directly into semi-supervised learning, energy function incorporating the conditional probability of classification is adopted. The semi-supervised learning is posed as the optimization of both the classification energy and the cluster compactness energy. The resulting integer programming is relaxed by a semidefinite relaxation where efficient solution can be obtained. Furthermore, the spectral graph methods provide improved energy minimization via the incorporation of additional criteria. Results on UCI datasets show promising results.The use of unlabeled data to aid classification is important as labeled data is often available in limited quantity. Instead of utilizing training samples directly into semi-supervised learning, energy function incorporating the conditional probability of classification is adopted. The semi-supervised learning is posed as the optimization of both the classification energy and the cluster compactness energy. The resulting integer programming is relaxed by a semidefinite relaxation where efficient solution can be obtained. Furthermore, the spectral graph methods provide improved energy minimization via the incorporation of additional criteria. Results on UCI datasets show promising results.