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Unsupervised, Supervised and Semi‐supervised Dimensionality Reduction by Low‐Rank Regression Analysis

Authors :
Kewei, TANG
Jun, ZHANG
Changsheng, ZHANG
Lijun, WANG
Yun, ZHAI
Wei, JIANG
Source :
Chinese Journal of Electronics; July 2021, Vol. 30 Issue: 4 p603-610, 8p
Publication Year :
2021

Abstract

Techniques for dimensionality reduction have attracted much attention in computer vision and pattern recognition. However, for the supervised or unsupervised case, the methods combining regression analysis and spectral graph analysis do not consider the global structure of the subspace; For semi‐supervised case, how to use the unlabeled samples more effectively is still an open problem. In this paper, we propose the methods by Low‐rank regression analysis (LRRA) to deal with these problems. For supervised or unsupervised dimensionality reduction, combining spectral graph analysis and LRRA can make a global constraint on the subspace. For semi‐supervised dimensionality reduction, the proposed method incorporating LRRA can exploit the unlabeled samples more effectively. The experimental results show the effectiveness of our methods.

Details

Language :
English
ISSN :
10224653 and 20755597
Volume :
30
Issue :
4
Database :
Supplemental Index
Journal :
Chinese Journal of Electronics
Publication Type :
Periodical
Accession number :
ejs57216818
Full Text :
https://doi.org/10.1049/cje.2021.05.002