Traditional data mining and machine learning technologies may fail when the training data and the testing data are drawn from different feature spaces and different distributions. Transfer learning, which uses the data from source domain and target domain, can tackle this problem. In this paper, we propose an improved Graph-based Model for Transfer learning (GM-Transfer). We construct a tripartite graph to represent the transfer learning problem and model the relations between the source domain data and the target domain data more efficiently. By learning the informational graph, the knowledge from the source domain data can be transferred to help improve the learning efficiency on the target domain data. Experiments show the effectiveness of our algorithm.
['Shizhun Yang', 'Chenping Hou', 'Yi Wu']
GM-transfer: Graph-based model for transfer learning