Semi-supervised Clustering with Deep Metric Learning

Abstract

Semi-supervised clustering has attracted lots of reserach interest due to its broad applications, and many methods have been presented. However there is still much space for improvement, (1) How to learn more discriminative feature representations to assist the traditional clustering methods; (2) How to make use of both the labeled and unlabelled data simultaneously and effectively during the process of clustering. To address these issues, we propose a novel semi-supervised clustering based on deep metric learning, namely SSCDML. By leveraging deep metric learning and semi-supervised learning effectively in a novel way, SSCDML dynamically update the unlabelled to labeled data through the limited labeled samples and obtain more meaningful data features, which make the classifier model more robust and the clustering results more accurate. Experimental results on Mnist, YaleB, and 20 Newsgroups databases demonstrate the high effectiveness of our proposed approach.