中文版 | English
Title

Discriminative subspace learning via optimization on Riemannian manifold

Author
Corresponding AuthorLiu,Quanying
Publication Years
2023-07-01
DOI
Source Title
ISSN
0031-3203
EISSN
1873-5142
Volume139
Abstract
Discriminative subspace learning is an important problem in machine learning, which aims to find the maximum separable decision subspace. Traditional Euclidean-based methods usually use Fisher discriminant criterion for finding an optimal linear mapping from a high-dimensional data space to a lower-dimensional subspace, which hardly guarantee a quadratic rate of global convergence and suffers from the singularity problem. Here, we propose the manifold optimization-based discriminant analysis (MODA) which is constructed by using the latent subspace alignment and the geometry of objective function with orthogonality constraint. MODA is solved by using Riemannian version of trust-region algorithm. Experimental results on various image datasets and electroencephalogram (EEG) datasets show that MODA achieves the best separability and is significantly superior to the competing algorithms. Especially for the time series of EEG signals, the accuracy of MODA is 20–30% higher than existing algorithms. The code for MODA is available at https://github.com/ncclabsustech/MODA-algorithm.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
First ; Corresponding
Funding Project
National Natural Science Foundation of China[62001205] ; National Key R&D Program of China[2021YFF1200804] ; Shenzhen Science and Technology Innovation Committee["2020 09251559570 04","KCXFZ2020122117340001","JCYJ20220818100213029"] ; Shenzhen-Hong Kong-Macao Science and Technology Innovation Project[SGDX2020110309280100] ; Guangdong Provincial Key Laboratory of Advanced Biomaterials[2022B1212010003]
WOS Research Area
Computer Science ; Engineering
WOS Subject
Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS Accession No
WOS:000954758500001
Publisher
ESI Research Field
ENGINEERING
Scopus EID
2-s2.0-85149684290
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/513351
DepartmentDepartment of Biomedical Engineering
Affiliation
1.Shenzhen Key Laboratory of Smart Healthcare Engineering,Department of Biomedical Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.School of Electronics and Information Technology,Sun Yat-sen University,Guangzhou,510006,China
First Author AffilicationDepartment of Biomedical Engineering
Corresponding Author AffilicationDepartment of Biomedical Engineering
First Author's First AffilicationDepartment of Biomedical Engineering
Recommended Citation
GB/T 7714
Yin,Wanguang,Ma,Zhengming,Liu,Quanying. Discriminative subspace learning via optimization on Riemannian manifold[J]. PATTERN RECOGNITION,2023,139.
APA
Yin,Wanguang,Ma,Zhengming,&Liu,Quanying.(2023).Discriminative subspace learning via optimization on Riemannian manifold.PATTERN RECOGNITION,139.
MLA
Yin,Wanguang,et al."Discriminative subspace learning via optimization on Riemannian manifold".PATTERN RECOGNITION 139(2023).
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