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Block-Row Sparse Multiview Multilabel Learning for Image Classification
Zhu, Xiaofeng1,2; Li, Xuelong3; Zhang, Shichao4; Zhang, SC
作者部门光学影像学习与分析中心
2016-02-01
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
卷号46期号:2页码:450-461
产权排序3
摘要In image analysis, the images are often represented by multiple visual features (also known as multiview features), that aim to better interpret them for achieving remarkable performance of the learning. Since the processes of feature extraction on each view are separated, the multiple visual features of images may include overlap, noise, and redundancy. Thus, learning with all the derived views of the data could decrease the effectiveness. To address this, this paper simultaneously conducts a hierarchical feature selection and a multiview multilabel (MVML) learning for multiview image classification, via embedding a proposed a new block-row regularizer into the MVML framework. The block-row regularizer concatenating a Frobenius norm (F-norm) regularizer and an l(2,1)-norm regularizer is designed to conduct a hierarchical feature selection, in which the F-norm regularizer is used to conduct a high-level feature selection for selecting the informative views (i.e., discarding the uninformative views) and the l(2,1)-norm regularizer is then used to conduct a low-level feature selection on the informative views. The rationale of the use of a block-row regularizer is to avoid the issue of the over-fitting (via the block-row regularizer), to remove redundant views and to preserve the natural group structures of data (via the F-norm regularizer), and to remove noisy features (the l(2,1)-norm regularizer), respectively. We further devise a computationally efficient algorithm to optimize the derived objective function and also theoretically prove the convergence of the proposed optimization method. Finally, the results on real image datasets show that the proposed method outperforms two baseline algorithms and three state-of-the-art algorithms in terms of classification performance.
文章类型Article
关键词Feature Selection Image Classification Joint Sparse Learning Machine Learning Multiview Learning
学科领域Computer Science, Artificial Intelligence
WOS标题词Science & Technology ; Technology
DOI10.1109/TCYB.2015.2403356
收录类别SCI ; EI
关键词[WOS]REGRESSION ; SELECTION
语种英语
WOS研究方向Computer Science
项目资助者China 863 Program(2012AA011005) ; China 973 Program(2013CB329404) ; Natural Science Foundation of China(61170131 ; Guangxi Natural Science Foundation for Teams of Innovation and Research(2012GXNSFGA060004) ; Guangxi 100 Plan ; Guangxi "Bagui" Teams for Innovation and Research ; National Natural Science Foundation of China(61125106) ; Key Research Program of Chinese Academy of Sciences(KGZD-EW-T03) ; 61450001 ; 61263035)
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号WOS:000370962900011
引用统计
被引频次:265[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/181661/27858
专题光谱成像技术研究室
通讯作者Zhang, SC
作者单位1.Xi An Jiao Tong Univ, Sch Math & Stat, Xian 710049, Peoples R China
2.Guangxi Normal Univ, Guangxi Key Lab Multisource Informat Min & Secur, Guilin 541004, Peoples R China
3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China
4.Zhejiang Gongshang Univ, Sch Comp Sci & Informat Technol, Hangzhou 310018, Zhejiang, Peoples R China
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GB/T 7714
Zhu, Xiaofeng,Li, Xuelong,Zhang, Shichao,et al. Block-Row Sparse Multiview Multilabel Learning for Image Classification[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(2):450-461.
APA Zhu, Xiaofeng,Li, Xuelong,Zhang, Shichao,&Zhang, SC.(2016).Block-Row Sparse Multiview Multilabel Learning for Image Classification.IEEE TRANSACTIONS ON CYBERNETICS,46(2),450-461.
MLA Zhu, Xiaofeng,et al."Block-Row Sparse Multiview Multilabel Learning for Image Classification".IEEE TRANSACTIONS ON CYBERNETICS 46.2(2016):450-461.
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