{"type":"ScholarlyArticle","title":"Robust Principal Component Analysis with Non-Greedy l1-Norm Maximization","authors":[{"name":"Feiping Nie","slug":"feiping-nie","given":"Feiping","family":"Nie","affiliation":""},{"name":"Heng Huang","slug":"heng-huang","given":"Heng","family":"Huang","affiliation":""},{"name":"Chris H. Q. Ding","slug":"chris-h-q-ding","given":"Chris H. Q.","family":"Ding","affiliation":""},{"name":"Dijun Luo","slug":"dijun-luo","given":"Dijun","family":"Luo","affiliation":""},{"name":"Hua Wang","slug":"hua-wang","given":"Hua","family":"Wang","affiliation":""}],"abstract":"Principal Component Analysis (PCA) is one of the most important methods to handle high-dimensional data. However, the high computa-tional complexity makes it hard to apply to the large scale data with high dimensionality, and the used 2-norm makes it sensitive to outliers. A recent work proposed principal component analysis based on 1-norm maximization, which is efficient and robust to outliers. In that work, a greedy strategy was applied due to the difficulty of directly solving the 1-norm maximization problem, which is easy to get stuck in local solution. In this paper, we first propose an efficient optimization algorithm to solve a general 1-norm maximization problem, and then propose a robust principal component analysis with non-greedy 1-norm maximization. Experimental results on real world datasets show that the non-greedy method always obtains much better solution than that of the greedy method.","doi":"10.5591/978-1-57735-516-8/IJCAI11-242","url":"https://ijcai.org/proceedings/2011/242","pdf":"https://ijcai.org/proceedings/2011/0242.pdf","markdown":"https://ijcai.org/proceedings/2011/242.md","bibtex":"https://ijcai.org/proceedings/2011/bibtex/242","volume":{"slug":"2011","title":"Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2011","url":"https://ijcai.org/proceedings/2011/","year":2011,"location":"Barcelona, Catalonia, Spain","dates":"16–22 July 2011","editors":"Toby Walsh","isbn":"978-1-57735-512-0; 978-1-57735-513-7; 978-1-57735-514-4; 978-1-57735-515-1; 978-1-57735-516-8","publisher":"AAAI Press/International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":242,"pages":{"first":1433,"last":1437},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}