Multi-label semi-supervised learning using regularized kernel spectral clustering

Abstract

Often in real-world applications such as web page categorization, automatic image annotations and protein function prediction, each instance is associated with multiple labels (categories) simultaneously. In addition, due to the labeling cost one usually deals with a large amount of unlabeled data while the fraction of labeled data points will typically be small. In this paper, we propose a multi-label semi-supervised kernel spectral clustering learning algorithm that learns from both labeled and unlabeled instances. The kernel spectral clustering algorithm (KSC) serves as a core model and the information of labeled data points is integrated into the model via regularization terms. The propagation of the multiple labels to unlabeled data points is achieved by incorporating the mutual correlation between (similarity across) labels as well as encouraging the model output to be as close as possible to the given ground-truth of the labeled data points. Thanks to the Nystrom approximation method, an explicit feature map is constructed and the optimization problem is solved in the primal. Experimental results demonstrate the effectiveness of the proposed approaches on real multi-label datasets.