{"type":"ScholarlyArticle","title":"GSPL: A Succinct Kernel Model for Group-Sparse Projections Learning of Multiview Data","authors":[{"name":"Danyang Wu","slug":"danyang-wu","given":"Danyang","family":"Wu","affiliation":""},{"name":"Jin Xu","slug":"jin-xu","given":"Jin","family":"Xu","affiliation":""},{"name":"Xia Dong","slug":"xia-dong","given":"Xia","family":"Dong","affiliation":""},{"name":"Meng Liao","slug":"meng-liao","given":"Meng","family":"Liao","affiliation":""},{"name":"Rong Wang","slug":"rong-wang","given":"Rong","family":"Wang","affiliation":""},{"name":"Feiping Nie","slug":"feiping-nie","given":"Feiping","family":"Nie","affiliation":""},{"name":"Xuelong Li","slug":"xuelong-li","given":"Xuelong","family":"Li","affiliation":""}],"abstract":"This paper explores a succinct kernel model for Group-Sparse Projections Learning (GSPL), to handle multiview feature selection task completely. Compared to previous works, our model has the following useful properties: 1) Strictness: GSPL innovatively learns group-sparse projections strictly on multiview data via ‘2;0-norm constraint, which is different with previous works that encourage group-sparse projections softly. 2) Adaptivity: In GSPL model, when the total number of selected features is given, the numbers of selected features of different views can be determined adaptively,\r\nwhich avoids artificial settings. Besides, GSPL can capture the differences among multiple views adaptively, which handles the inconsistent problem among different views. 3) Succinctness: Except for the intrinsic parameters of projection-based feature selection task, GSPL does not bring extra parameters, which guarantees the applicability in practice. To solve the optimization problem involved in GSPL, a novel iterative algorithm is proposed with rigorously theoretical guarantees. Experimental results demonstrate the superb performance of GSPL on synthetic and real datasets.","doi":"10.24963/ijcai.2021/438","url":"https://ijcai.org/proceedings/2021/438","pdf":"https://ijcai.org/proceedings/2021/0438.pdf","markdown":"https://ijcai.org/proceedings/2021/438.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/438","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":438,"pages":{"first":3185,"last":3191},"keywords":["Machine Learning: Learning Sparse Models","Machine Learning: Unsupervised Learning","Machine Learning: Multi-instance; Multi-label; Multi-view learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}