{"type":"ScholarlyArticle","title":"On Multiple Kernel Learning with Multiple Labels","authors":[{"name":"Lei Tang","slug":"lei-tang","given":"Lei","family":"Tang","affiliation":""},{"name":"Jianhui Chen","slug":"jianhui-chen","given":"Jianhui","family":"Chen","affiliation":""},{"name":"Jieping Ye","slug":"jieping-ye","given":"Jieping","family":"Ye","affiliation":""}],"abstract":"For classification with multiple labels, a common approach is to learn a classifier for each label. With a kernel-based classifier, there are two options to set up kernels: select a specific kernel for each label or the same kernel for all labels. In this work, we present a unified framework for multi-label multiple kernel learning, in which the above two approaches can be considered as two extreme cases. Moreover, our framework allows the kernels shared partially among multiple labels, enabling flexible degrees of label commonality. We systematically study how the sharing of kernels among multiple labels affects the performance based on extensive experiments on various benchmark data including images and microarray data. Interesting findings concerning efficacy and efficiency are reported.Lei Tang, Jianhui Chen, Jieping Ye","doi":null,"url":"https://ijcai.org/proceedings/2009/211","pdf":"https://ijcai.org/proceedings/2009/0211.pdf","markdown":"https://ijcai.org/proceedings/2009/211.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/211","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":211,"pages":{"first":1255,"last":1259},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}