{"type":"ScholarlyArticle","title":"Online Heterogeneous Transfer Metric Learning","authors":[{"name":"Yong Luo","slug":"yong-luo","given":"Yong","family":"Luo","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"},{"name":"Tongliang Liu","slug":"tongliang-liu","given":"Tongliang","family":"Liu","affiliation":"UBTECH Sydney AI Centre , SIT , FEIT , University of Sydney , Australia"},{"name":"Yonggang Wen","slug":"yonggang-wen","given":"Yonggang","family":"Wen","affiliation":"School of Computer Science and Engineering , Nanyang Technological University , Singapore"},{"name":"Dacheng Tao","slug":"dacheng-tao","given":"Dacheng","family":"Tao","affiliation":"UBTECH Sydney AI Centre , SIT , FEIT , University of Sydney , Australia"}],"abstract":"Distance metric learning (DML) has been demonstrated to be successful and essential in diverse applications. Transfer metric learning (TML) can help DML in the target domain with limited label information by utilizing information from some related source domains. The heterogeneous TML (HTML), where the feature representations vary from the source to the target domain, is general and challenging. However, current HTML approaches are usually conducted in a batch manner and cannot handle sequential data. This motivates the proposed online HTML (OHTML) method. In particular, the distance metric in the source domain is pre-trained using some existing DML algorithms. To enable knowledge transfer, we assume there are large amounts of unlabeled corresponding data that have representations in both the source and target domains. By enforcing the distances (between these unlabeled samples) in the target domain to agree with those in the source domain under the manifold regularization theme, we learn an improved target metric. We formulate the problem in the online setting so that the optimization is efficient and the model can be adapted to new coming data. Experiments in diverse applications demonstrate both effectiveness and efficiency of the proposed method.","doi":"10.24963/ijcai.2018/350","url":"https://ijcai.org/proceedings/2018/350","pdf":"https://ijcai.org/proceedings/2018/0350.pdf","markdown":"https://ijcai.org/proceedings/2018/350.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/350","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":350,"pages":{"first":2525,"last":2531},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Online Learning"],"videos":[],"references":[{"position":1,"raw":"[Belkin et al., 2006] Mikhail Belkin, Partha Niyogi, and Vikas Sindhwani. 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