{"type":"ScholarlyArticle","title":"Online Multitask Relative Similarity Learning","authors":[{"name":"Shuji Hao","slug":"shuji-hao","given":"Shuji","family":"Hao","affiliation":"Institute of High Performance Computing , A"},{"name":"Peilin Zhao","slug":"peilin-zhao","given":"Peilin","family":"Zhao","affiliation":"Articial Intelligence Department , Ant Financial Services Group , China"},{"name":"Yong Liu","slug":"yong-liu","given":"Yong","family":"Liu","affiliation":"Institute for Infocomm Research , A*STAR , Singapore"},{"name":"Steven C. H. Hoi","slug":"steven-c-h-hoi","given":"Steven C. H.","family":"Hoi","affiliation":"School of Information Systems , SMU , Singapore; *STAR , Singapore"},{"name":"Chunyan Miao","slug":"chunyan-miao","given":"Chunyan","family":"Miao","affiliation":"School of Computer Science and Engineering , NTU , Singapore"}],"abstract":"Relative similarity learning~(RSL) aims to learn similarity functions from data with relative constraints. Most previous algorithms developed for RSL are batch-based learning approaches which suffer from poor scalability when dealing with real-world data arriving sequentially. These methods are often designed to learn a single similarity function for a specific task. Therefore, they may be sub-optimal to solve multiple task learning problems. To overcome these limitations, we propose a scalable RSL framework named OMTRSL (Online Multi-Task  Relative Similarity Learning). Specifically, we first develop a simple yet effective online learning algorithm for multi-task relative similarity learning. Then, we also propose an active learning algorithm to save the labeling cost. The proposed algorithms not only enjoy theoretical guarantee, but also show high efficacy and efficiency in extensive experiments on real-world datasets.","doi":"10.24963/ijcai.2017/253","url":"https://ijcai.org/proceedings/2017/253","pdf":"https://ijcai.org/proceedings/2017/0253.pdf","markdown":"https://ijcai.org/proceedings/2017/253.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/253","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":253,"pages":{"first":1823,"last":1829},"keywords":["Machine Learning: Online Learning","Machine Learning: Time-series/Data Streams"],"videos":[],"references":[{"position":1,"raw":"[Ammar et al., 2014] Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, and Matthew Taylor. 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