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题名:
Regularized Label Relaxation Linear Regression
作者: Fang, Xiaozhao1; Xu, Yong2,3; Li, Xuelong4; Lai, Zhihui5; Wong, Wai Keung6,7; Fang, Bingwu8
作者部门: 光学影像学习与分析中心
刊名: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期: 2018-04
卷号: 29, 期号:4, 页码:1006-1018
关键词: Class compactness graph ; computer vision ; label relaxation ; linear regression (LR) ; manifold learning
学科分类: Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
DOI: 10.1109/TNNLS.2017.2648880
通讯作者: Xu, Y (reprint author), Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Peoples R China.
英文摘要:

Linear regression (LR) and some of its variants have been widely used for classification problems. Most of these methods assume that during the learning phase, the training samples can be exactly transformed into a strict binary label matrix, which has too little freedom to fit the labels adequately. To address this problem, in this paper, we propose a novel regularized label relaxation LR method, which has the following notable characteristics. First, the proposed method relaxes the strict binary label matrix into a slack variable matrix by introducing a nonnegative label relaxation matrix into LR, which provides more freedom to fit the labels and simultaneously enlarges the margins between different classes as much as possible. Second, the proposed method constructs the class compactness graph based on manifold learning and uses it as the regularization item to avoid the problem of overfitting. The class compactness graph is used to ensure that the samples sharing the same labels can be kept close after they are transformed. Two different algorithms, which are, respectively, based on l(2)-norm and l(2,1)-norm loss functions are devised. These two algorithms have compact closed-form solutions in each iteration so that they are easily implemented. Extensive experiments show that these two algorithms outperform the state-of-the-art algorithms in terms of the classification accuracy and running time.

研究领域[WOS]: Computer Science ; Engineering
收录类别: SCI
语种: 英语
WOS记录号: WOS:000427859600020
ISSN号: 2162-237X
产权排序: 4
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.opt.ac.cn/handle/181661/30016
Appears in Collections:光学影像学习与分析中心_期刊论文

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作者单位: 1.Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 518055, Guangdong, Peoples R China
2.Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Peoples R China
3.Key Lab Network Oriented Intelligent Computat, Shenzhen 518055, Peoples R China
4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPTical Magery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
5.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518055, Peoples R China
6.Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China
7.Hong Kong Polytech Univ, Shenzhen Res Inst, Shenzhen, Peoples R China
8.Nanjing Univ Aeronaut & Astronaut, Sch Comp Sci & Technol, Nanjing, Jiangsu, Peoples R China

Recommended Citation:
Fang, Xiaozhao,Xu, Yong,Li, Xuelong,et al. Regularized Label Relaxation Linear Regression[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018-04-01,29(4):1006-1018.
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