Title | Discriminative subspace learning via optimization on Riemannian manifold |
Author | |
Corresponding Author | Liu,Quanying |
Publication Years | 2023-07-01
|
DOI | |
Source Title | |
ISSN | 0031-3203
|
EISSN | 1873-5142
|
Volume | 139 |
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 Type | Journal Article |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/513351 |
Department | Department 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 Affilication | Department of Biomedical Engineering |
Corresponding Author Affilication | Department of Biomedical Engineering |
First Author's First Affilication | Department 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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