A community discovery approach in multi-label data
Na Li, Zhisong Pan, Mingchu Jiang, Yanyan Zhang, Haimin Yang · 2016
Multi-label learning is popular in current research of machine learning areas, and there have already been many methods using label relationship to solve multi-label problems. However, the meaning of their relationship is not so obvious that it's hard for us to know the fact among labels. Besides, with the development of multi-label learning, hierarchical multi-label classification is a new research hotspot in the field of machine learning and data mining, that these labels have a hierarchical structure, so it is more and more essential to analyze the structure of them to help us learn the deep meaning of labels. In this paper, we proposed a community discovery approach based on label relationship and NMF algorithm both in common multi-label datasets and hierarchical datasets. With this method, we can find the clear structure in multiple labels, which is helpful for obtaining the label community and catching sight of regulation these tags. After all, it's of great importance for the study of multi-label relationship learning.