Semi-supervised Multi-label Learning by Solving a Sylvester Equation

Multi-label learning refers to the problems where an instance can be assigned to more than one category. In this paper, we present a novel Semi-supervised algorithm for Multi-label learning by solving a Sylvester Equation (SMSE). Two graphs are first constructed on instance level and category level respectively. For instance level, a graph is defined based on both labeled and unlabeled instances, where each node represents one instance and each edge weight reflects the similarity between corresponding pairwise instances. Similarly, for category level, a graph is also built based on all the categories, where each node represents one category and each edge weight reflects the similarity between corresponding pairwise categories. A regularization framework combining two regularization terms for the two graphs is suggested. The regularization term for instance graph measures the smoothness of the labels of instances, and the regularization term for category graph measures the smoothness o...

• Type: Conference
• Year: 2008
• Where: SDM
• Authors: Gang Chen, Yangqiu Song, Fei Wang, Changshui Zhang
• Keywords: Data Mining, Multi-label Learning, Regularization Term, SDM 2008, Sylvester Equation