{"type":"ScholarlyArticle","title":"A Joint Optimization Framework of Sparse Coding and Discriminative Clustering","authors":[{"name":"Zhangyang Wang","slug":"zhangyang-wang","given":"Zhangyang","family":"Wang","affiliation":""},{"name":"Yingzhen Yang","slug":"yingzhen-yang","given":"Yingzhen","family":"Yang","affiliation":""},{"name":"Shiyu Chang","slug":"shiyu-chang","given":"Shiyu","family":"Chang","affiliation":""},{"name":"Jinyan Li","slug":"jinyan-li","given":"Jinyan","family":"Li","affiliation":""},{"name":"Simon Fong","slug":"simon-fong","given":"Simon","family":"Fong","affiliation":""},{"name":"Thomas S. Huang","slug":"thomas-s-huang","given":"Thomas S.","family":"Huang","affiliation":""}],"abstract":"Many clustering methods highly depend on extracted features. In this paper, we propose a joint optimization framework in terms of both feature extraction and discriminative clustering. We utilize graph regularized sparse codes as the features, and formulate sparse coding as the constraint for clustering. Two cost functions are developed based on entropy-minimization and maximum-margin clustering principles, respectively, as the objectives to be minimized. Solving such a bi-level optimization mutually reinforces both sparse coding and clustering steps. Experiments on several benchmark datasets verify remarkable performance improvements led by the proposed joint optimization.","doi":null,"url":"https://ijcai.org/proceedings/2015/552","pdf":"https://ijcai.org/proceedings/2015/0552.pdf","markdown":"https://ijcai.org/proceedings/2015/552.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/552","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":552,"pages":{"first":3932,"last":3937},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}