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Approximate Low-Rank Projection Learning for Feature Extraction
Fang, Xiaozhao1; Han, Na1; Wu, Jigang1; Xu, Yong2,3; Yang, Jian4; Wong, Wai Keung5,6; Li, Xuelong7,8
作者部门光学影像学习与分析中心
2018-11
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X;2162-2388
卷号29期号:11页码:5228-5241
产权排序7
摘要

Feature extraction plays a significant role in pattern recognition. Recently, many representation-based feature extraction methods have been proposed and achieved successes in many applications. As an excellent unsupervised feature extraction method, latent low-rank representation (LatLRR) has shown its power in extracting salient features. However, LatLRR has the following three disadvantages: 1) the dimension of features obtained using LatLRR cannot be reduced, which is not preferred in feature extraction; 2) two low-rank matrices are separately learned so that the overall optimality may not be guaranteed; and 3) LatLRR is an unsupervised method, which by far has not been extended to the supervised scenario. To this end, in this paper, we first propose to use two different matrices to approximate the low-rank projection in LatLRR so that the dimension of obtained features can be reduced, which is more flexible than original LatLRR. Then, we treat the two low-rank matrices in LatLRR as a whole in the process of learning. In this way, they can be boosted mutually so that the obtained projection can extract more discriminative features. Finally, we extend LatLRR to the supervised scenario by integrating feature extraction with the ridge regression. Thus, the process of feature extraction is closely related to the classification so that the extracted features are discriminative. Extensive experiments are conducted on different databases for unsupervised and supervised feature extraction, and very encouraging results are achieved in comparison with many state-of-the-arts methods.

关键词Computer Vision Feature Extraction Low-rank Representation (Lrr) Pattern Recognition Ridge Regression
DOI10.1109/TNNLS.2018.2796133
收录类别SCI
语种英语
WOS记录号WOS:000447832200005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30693
专题光学影像学习与分析中心
通讯作者Han, Na
作者单位1.Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, 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.Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China
5.Hong Kong Polytech Univ, Inst Text & Clothing, Hong Kong, Hong Kong, Peoples R China
6.Hong Kong Polytech Univ, Shenzhen Res Inst, Shenzhen 518055, Peoples R China
7.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
8.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Fang, Xiaozhao,Han, Na,Wu, Jigang,et al. Approximate Low-Rank Projection Learning for Feature Extraction[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(11):5228-5241.
APA Fang, Xiaozhao.,Han, Na.,Wu, Jigang.,Xu, Yong.,Yang, Jian.,...&Li, Xuelong.(2018).Approximate Low-Rank Projection Learning for Feature Extraction.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(11),5228-5241.
MLA Fang, Xiaozhao,et al."Approximate Low-Rank Projection Learning for Feature Extraction".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.11(2018):5228-5241.
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