Open AccessUnsupervised, Supervised and Semi‐supervised Dimensionality Reduction by Low‐Rank Regression Analysis
Author(s) -
Kewei TANG,
Jun ZHANG,
Changsheng ZHANG,
Lijun WANG,
Yun ZHAI,
Wei JIANG
Publication year - 2021
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2021.05.002
Subject(s) - dimensionality reduction , subspace topology , artificial intelligence , pattern recognition (psychology) , graph , computer science , regression , curse of dimensionality , rank (graph theory) , reduction (mathematics) , semi supervised learning , regression analysis , machine learning , mathematics , statistics , geometry , combinatorics , theoretical computer science
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.