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Regularized Label Relaxation Linear Regression
Fang, Xiaozhao1; Xu, Yong2,3; Li, Xuelong4; Lai, Zhihui5; Wong, Wai Keung6,7; Fang, Bingwu8; Xu, Y (reprint author), Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Peoples R China.
作者部门光学影像学习与分析中心
2018-04-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
卷号29期号:4页码:1006-1018
产权排序4
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.
文章类型Article
关键词Class Compactness Graph Computer Vision Label Relaxation Linear Regression (Lr) Manifold Learning
学科领域Computer Science, Artificial Intelligence
WOS标题词Science & Technology ; Technology
DOI10.1109/TNNLS.2017.2648880
收录类别SCI
关键词[WOS]SPARSE REPRESENTATION ; FACE RECOGNITION ; DIMENSIONALITY REDUCTION ; DISCRIMINANT-ANALYSIS ; CLASSIFICATION ; FRAMEWORK ; GRAPH ; SELECTION
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000427859600020
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30016
专题光学影像学习与分析中心
通讯作者Xu, Y (reprint author), Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Peoples R China.
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
推荐引用方式
GB/T 7714
Fang, Xiaozhao,Xu, Yong,Li, Xuelong,et al. Regularized Label Relaxation Linear Regression[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(4):1006-1018.
APA Fang, Xiaozhao.,Xu, Yong.,Li, Xuelong.,Lai, Zhihui.,Wong, Wai Keung.,...&Xu, Y .(2018).Regularized Label Relaxation Linear Regression.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(4),1006-1018.
MLA Fang, Xiaozhao,et al."Regularized Label Relaxation Linear Regression".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.4(2018):1006-1018.
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