{"type":"ScholarlyArticle","title":"Probabilistic Multi-Label Classification with Sparse Feature Learning","authors":[{"name":"Yuhong Guo","slug":"yuhong-guo","given":"Yuhong","family":"Guo","affiliation":""},{"name":"Wei Xue","slug":"wei-xue","given":"Wei","family":"Xue","affiliation":""}],"abstract":"Multi-label classification is a critical problem in many areas of data analysis such as image labeling and text categorization. In this paper we propose a probabilistic multi-label classification model based on novel sparse feature learning. By employing an individual sparsity inducing ℓ1-norm and a group sparsity inducing ℓ2,1-norm, the proposed model has the capacity of capturing both label interdependencies and common predictive model structures. We formulate this sparse norm regularized learning problem as a non-smooth convex optimization problem, and develop a fast proximal gradient algorithm to solve it for an optimal solution. Our empirical study demonstrates the efficacy of the proposed method on a set of multi-label tasks given a limited number of labeled training instances.","doi":null,"url":"https://ijcai.org/proceedings/2013/206","pdf":"https://ijcai.org/proceedings/2013/0206.pdf","markdown":"https://ijcai.org/proceedings/2013/206.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/206","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":206,"pages":{"first":1373,"last":1378},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}