{"type":"ScholarlyArticle","title":"Efficient Kernel Learning from Side Information Using ADMM","authors":[{"name":"En-Liang Hu","slug":"en-liang-hu","given":"En-Liang","family":"Hu","affiliation":""},{"name":"James T. Kwok","slug":"james-t-kwok","given":"James T.","family":"Kwok","affiliation":""}],"abstract":"Side information is highly useful in the learning of a nonparametric kernel matrix. However, this often leads to an expensive semidefinite program (SDP). In recent years, a umber of dedicated solvers have been proposed. Though much better than off-the-shelf SDP solvers, they still cannot scale to large data sets. In this paper, we propose a novel solver based on the alternating direction method of multipliers (ADMM). The key idea is to use a low-rank decomposition of the kernel matrix K = VT U, with the constraint that V = U. The resultant optimization problem, though non-convex, has favorable convergence properties and can be efficiently solved without requiring eigen-decomposition in each iteration. Experimental results on a number of real-world data sets demonstrate that the proposed method is as accurate as directly solving the SDP, but can be one to two orders of magnitude faster.","doi":null,"url":"https://ijcai.org/proceedings/2013/211","pdf":"https://ijcai.org/proceedings/2013/0211.pdf","markdown":"https://ijcai.org/proceedings/2013/211.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/211","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":211,"pages":{"first":1408,"last":1413},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}