More about HKUST
A Survey on Heterogeneous Transfer Learning
PhD Qualifying Examination Title: "A Survey on Heterogeneous Transfer Learning" by Miss Ying WEI Abstract: Transfer learning algorithms have been proposed to improve the learning performance of the target domain where usually labelled data are scarce, under the help of the source domain where we have a large amount of labelled data. The source domain and target domain usually are not the same, otherwise the problem degenerates to traditional machine learning. As transfer learning defines, the discrepancy between the target and source domain could be any of the data distribution, feature space, label space, and predictive function mismatches. In this survey, we focus on the case where the source and target domain lie in different feature spaces or label spaces. Yang et al. [60] initiated to name the setting as "heterogeneous transfer learning". In the big data era, heterogeneity is prevalent given the boom of varieties of data, such as images, audio, text and so on. Heterogeneous transfer learning enables knowledge transfer among these data sources which probably lie in incommensurable feature spaces or disparate label spaces. To the best of my knowledge, this survey is the first to systematically review related work on heterogeneous transfer learning. We discuss the relationship between heterogeneous transfer learning and previous transfer learning. Besides, we investigate heterogeneous transfer learning's performances on three tasks, i.e., transfer for classification, transfer for clustering and transfer for understanding. We also present and categorize a bunch of techniques that are frequently employed by heterogeneous transfer learning. Finally, we examine a list of applications that heterogeneous transfer learning already or potentially pays off. Date: Tuesday, 8 November 2016 Time: 2:30pm - 4:30pm Venue: Room 3501 Lifts 25/26 Committee Members: Prof. Qiang Yang (Supervisor) Prof. Prof. Nevin Zhang (Chairperson) Prof. Lei Chen Dr. Yangqiu Song **** ALL are Welcome ****