Semi-supervised hierarchy learning using multiple-labeled data

While hierarchical semi-supervised classification methods have been previously studied, we still lack an algorithm that can learn a non-predefined categorical hierarchy from multi-labeled data at various levels of specificity. Inspired by human psychology and learning experience, in this paper we propose a semi-supervised learning method that can classify multi-labeled data into a hierarchy based on the label´s specificity level such that the separability between each class and its siblings is greater than the separability between each class and its parents. To build the hierarchy we show that a minimum spanning tree minimizes an upper bound on the pairwise Kullback-Liebler divergence between the true and approximated distributions. We show the effectiveness of our method using three types of data sets and draw a comparison between our learned hierarchy and one learned by human subjects using the same data set. We also show the effectiveness of our method compared to hierarchical clustering.