{"type":"ScholarlyArticle","title":"Probit Classifiers with a Generalized Gaussian Scale Mixture Prior","authors":[{"name":"Guoqing Liu","slug":"guoqing-liu","given":"Guoqing","family":"Liu","affiliation":""},{"name":"Jianxin Wu","slug":"jianxin-wu","given":"Jianxin","family":"Wu","affiliation":""},{"name":"Suiping Zhou","slug":"suiping-zhou","given":"Suiping","family":"Zhou","affiliation":""}],"abstract":"Most of the existing probit classifiers are based on sparsity-oriented modeling. However, we show that sparsity is not always desirable in practice, and only an appropriate degree of sparsity is profitable. In this work, we propose a flexible probabilistic model using a generalized Gaussian scale mixture prior that can promote an appropriate degree of sparsity for its model parameters, and yield either sparse or non-sparse estimates according to the intrinsic sparsity of features in a dataset. Model learning is carried out by an efficient modified maximum a posteriori (MAP) estimate. We also show relationships of the proposed model to existing probit classifiers as well as iteratively re-weighted l1 and l2 minimizations. Experiments demonstrate that the proposed method has better or comparable performances in feature selection for linear classifiers as well as in kernel-based classification.","doi":"10.5591/978-1-57735-516-8/IJCAI11-232","url":"https://ijcai.org/proceedings/2011/232","pdf":"https://ijcai.org/proceedings/2011/0232.pdf","markdown":"https://ijcai.org/proceedings/2011/232.md","bibtex":"https://ijcai.org/proceedings/2011/bibtex/232","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":232,"pages":{"first":1372,"last":1376},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}