{"type":"ScholarlyArticle","title":"Transfer Hashing with Privileged Information","authors":[{"name":"Joey Tianyi Zhou","slug":"joey-tianyi-zhou","given":"Joey Tianyi","family":"Zhou","affiliation":""},{"name":"Xinxing Xu","slug":"xinxing-xu","given":"Xinxing","family":"Xu","affiliation":""},{"name":"Sinno Jialin Pan","slug":"sinno-jialin-pan","given":"Sinno Jialin","family":"Pan","affiliation":""},{"name":"Ivor W. Tsang","slug":"ivor-w-tsang","given":"Ivor W.","family":"Tsang","affiliation":""},{"name":"Zheng Qin","slug":"zheng-qin","given":"Zheng","family":"Qin","affiliation":""},{"name":"Rick Siow Mong Goh","slug":"rick-siow-mong-goh","given":"Rick Siow Mong","family":"Goh","affiliation":""}],"abstract":"Most existing learning to hash methods assume that there are sufficient data, either labeled or unlabeled, on the domain of interest (i.e., the target domain) for training. However, this assumption cannot be satisfied in some real-world applications. To address this data sparsity issue in hashing, inspired by transfer learning, we propose a new framework named Transfer Hashing with Privileged Information (THPI). Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+. In ITQ+, a new slack function is learned from auxiliary data to approximate the quantization error in ITQ. We developed an alternating optimization approach to solve the resultant optimization problem for ITQ+. We further extend ITQ+ to LapITQ+ by utilizing the geometry structure among the auxiliary data for learning more precise binary codes in the target domain. Extensive experiments on several benchmark datasets verify the effectiveness of our proposed approaches through comparisons with several state-of-the-art baselines.","doi":null,"url":"https://ijcai.org/proceedings/2016/344","pdf":"https://ijcai.org/proceedings/2016/0344.pdf","markdown":"https://ijcai.org/proceedings/2016/344.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/344","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":344,"pages":{"first":2414,"last":2419},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}