{"type":"ScholarlyArticle","title":"Multi-View Learning with Limited and Noisy Tagging","authors":[{"name":"Yingming Li","slug":"yingming-li","given":"Yingming","family":"Li","affiliation":""},{"name":"Ming Yang","slug":"ming-yang","given":"Ming","family":"Yang","affiliation":""},{"name":"Zenglin Xu","slug":"zenglin-xu","given":"Zenglin","family":"Xu","affiliation":""},{"name":"Zhongfei Zhang","slug":"zhongfei-zhang","given":"Zhongfei","family":"Zhang","affiliation":""}],"abstract":"Multi-view tagging has become increasingly popular in the applications where data representations by multiple views exist. A robust multi-view tagging method must have the capability to meet the two challenging requirements: limited labeled training samples and noisy labeled training samples. In this paper, we investigate this challenging problem of learning with limited and noisy tagging and propose a discriminative model, called MSMC, that exploits both labeled and unlabeled data through a semi-parametric regularization and takes advantage of the multi-label space consistency into the optimization. While MSMC is a general method for learning with multi-view, limited, and noisy tagging, in the evaluations we focus on the specific application of noisy image tagging with limited labeled training samples on a benchmark dataset. Extensive evaluations in comparison with state-of-the-art literature demonstrate that MSMC outstands with a superior performance.","doi":null,"url":"https://ijcai.org/proceedings/2016/246","pdf":"https://ijcai.org/proceedings/2016/0246.pdf","markdown":"https://ijcai.org/proceedings/2016/246.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/246","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":246,"pages":{"first":1718,"last":1723},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}