OPT OpenIR  > 光学影像学习与分析中心
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
Department光学影像学习与分析中心
2018-11
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X;2162-2388
Volume29Issue:11Pages:5228-5241
Contribution Rank7
Abstract

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.

KeywordComputer Vision Feature Extraction Low-rank Representation (Lrr) Pattern Recognition Ridge Regression
DOI10.1109/TNNLS.2018.2796133
Indexed BySCI ; EI
Language英语
WOS IDWOS:000447832200005
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI Accession Number20180704801548
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30693
Collection光学影像学习与分析中心
Corresponding AuthorHan, Na
Affiliation1.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
Recommended Citation
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.
Files in This Item:
File Name/Size DocType Version Access License
Approximate Low-Rank(2693KB)期刊论文出版稿限制开放CC BY-NC-SAApplication Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Fang, Xiaozhao]'s Articles
[Han, Na]'s Articles
[Wu, Jigang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Fang, Xiaozhao]'s Articles
[Han, Na]'s Articles
[Wu, Jigang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Fang, Xiaozhao]'s Articles
[Han, Na]'s Articles
[Wu, Jigang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.