Joint Multilabel Classification With Community-Aware Label Graph Learning
文献类型：期刊论文
| 作者 | Li, Xi1; Zhao, Xueyi2,3; Zhang, Zhongfei2,4; Wu, Fei1; Zhuang, Yueting1; Wang, Jingdong5; Li, Xuelong6
|
| 刊名 | ieee transactions on image processing
 |
| 出版日期 | 2016 |
| 卷号 | 25期号:1页码:484-493 |
| 关键词 | Supervised learning classification algorithms support vector machines |
| ISSN号 | 1057-7149 |
| 产权排序 | 6 |
| 通讯作者 | zhao, xy |
| 英文摘要 | as an important and challenging problem in machine learning and computer vision, multilabel classification is typically implemented in a max-margin multilabel learning framework, where the inter-label separability is characterized by the sample-specific classification margins between labels. however, the conventional multilabel classification approaches are usually incapable of effectively exploring the intrinsic inter-label correlations as well as jointly modeling the interactions between inter-label correlations and multilabel classification. to address this issue, we propose a multilabel classification framework based on a joint learning approach called label graph learning (lgl) driven weighted support vector machine (svm). in principle, the joint learning approach explicitly models the inter-label correlations by lgl, which is jointly optimized with multilabel classification in a unified learning scheme. as a result, the learned label correlation graph well fits the multilabel classification task while effectively reflecting the underlying topological structures among labels. moreover, the inter-label interactions are also influenced by label-specific sample communities (each community for the samples sharing a common label). namely, if two labels have similar label-specific sample communities, they are likely to be correlated. based on this observation, lgl is further regularized by the label hypergraph laplacian. experimental results have demonstrated the effectiveness of our approach over several benchmark data sets. |
| WOS标题词 | science & technology ; technology |
| 学科主题 | computer science, artificial intelligence ; engineering, electrical & electronic |
| 类目[WOS] | computer science, artificial intelligence ; engineering, electrical & electronic |
| 研究领域[WOS] | computer science ; engineering |
| 关键词[WOS] | image classification ; categorization |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000367257100004 |
| 公开日期 | 2016-02-25 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/27738]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Zhejiang, Peoples R China 2.Zhejiang Univ, Dept Informat Sci & Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China 3.China Acad Elect & Informat Technol, Beijing 100041, Peoples R China 4.SUNY Binghamton, Watson Sch, Dept Comp Sci, Binghamton, NY 13902 USA 5.Microsoft Res Asia, Visual Comp Grp, Beijing 100080, Peoples R China 6.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Xi,Zhao, Xueyi,Zhang, Zhongfei,et al. Joint Multilabel Classification With Community-Aware Label Graph Learning[J]. ieee transactions on image processing,2016,25(1):484-493. |
| APA | Li, Xi.,Zhao, Xueyi.,Zhang, Zhongfei.,Wu, Fei.,Zhuang, Yueting.,...&Li, Xuelong.(2016).Joint Multilabel Classification With Community-Aware Label Graph Learning.ieee transactions on image processing,25(1),484-493. |
| MLA | Li, Xi,et al."Joint Multilabel Classification With Community-Aware Label Graph Learning".ieee transactions on image processing 25.1(2016):484-493. |
入库方式： OAI收割
来源：西安光学精密机械研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。