Joint Multilabel Classification With Community-Aware Label Graph Learning

doi:10.1109/TIP.2015.2503700

OPT OpenIR > 光谱成像技术研究室

	Joint Multilabel Classification With Community-Aware Label Graph Learning
	Li, Xi 1; Zhao, Xueyi 2,3; Zhang, Zhongfei 2,4; Wu, Fei 1; Zhuang, Yueting 1; Wang, Jingdong 5; Li, Xuelong6 ; Zhao, XY
作者部门	光学影像学习与分析中心
	2016
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	25 期号:1 页码:484-493
产权排序	6
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.
文章类型	Article
关键词	Supervised Learning Classification Algorithms Support Vector Machines
学科领域	Computer Science, Artificial Intelligence
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TIP.2015.2503700
收录类别	SCI
关键词[WOS]	IMAGE CLASSIFICATION ; CATEGORIZATION
语种	英语
WOS研究方向	Computer Science ; Engineering
项目资助者	National Natural Science Foundation of China(61472353) ; China Knowledge Centre for Engineering Sciences and Technology ; Key Research Program, Chinese Academy of Sciences(KGZD-EW-T03) ; National Basic Research Program of China(2012CB316400 ; Fundamental Research Funds for the Central Universities ; Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis ; Microsoft Research Asia Collaborative Research Program through the Ministry of Education-Microsoft Key Laboratory, Zhejiang University ; U.S. National Science Foundation(CCF-1017828) ; 2015CB352300)
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000367257100004
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/27738
专题	光谱成像技术研究室
通讯作者	Zhao, XY
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.,...&Zhao, XY.(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.

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