{"type":"ScholarlyArticle","title":"Greedy Learning of Generalized Low-Rank Models","authors":[{"name":"Quanming Yao","slug":"quanming-yao","given":"Quanming","family":"Yao","affiliation":""},{"name":"James T. Kwok","slug":"james-t-kwok","given":"James T.","family":"Kwok","affiliation":""}],"abstract":"Learning of low-rank matrices is fundamental to many machine learning applications. A state-of-the-art algorithm is the rank-one matrix pursuit (R1MP). However, it can only be used in matrix completion problems with the square loss. In this paper, we develop a more flexible greedy algorithm for generalized low-rank models whose optimization objective can be smooth or nonsmooth, general convex or strongly convex. The proposed algorithm has low per-iteration time complexity and fast convergence rate.Experimental results show that it is much faster than the state-of-the-art,with comparable or even better prediction performance.","doi":null,"url":"https://ijcai.org/proceedings/2016/327","pdf":"https://ijcai.org/proceedings/2016/0327.pdf","markdown":"https://ijcai.org/proceedings/2016/327.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/327","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":327,"pages":{"first":2294,"last":2299},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}