Discriminative subspace learning via optimization on Riemannian manifold

Yin，Wanguang1; Ma，Zhengming2; Liu，Quanying1

2023-07-01

10.1016/j.patcog.2023.109450

PATTERN RECOGNITION   影响因子和分区

0031-3203

1873-5142

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.

Classification Dimensionality reduction Discriminative subspace learning Riemannian manifold optimization

SCI ; EI

英语

第一 ; 通讯

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]

Computer Science ; Engineering

Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic

WOS:000954758500001

ELSEVIER SCI LTD

20231113704422

Clustering algorithms ; Electroencephalography ; Geometry ; Learning algorithms ; Learning systems

Medicine and Pharmacology:461.6 ; Machine Learning:723.4.2 ; Information Sources and Analysis:903.1 ; Mathematics:921 ; Statistical Methods:922

ENGINEERING

2-s2.0-85149684290

Scopus

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

Yin，Wanguang,Ma，Zhengming,Liu，Quanying. Discriminative subspace learning via optimization on Riemannian manifold[J]. PATTERN RECOGNITION,2023,139.

Yin，Wanguang,Ma，Zhengming,&Liu，Quanying.(2023).Discriminative subspace learning via optimization on Riemannian manifold.PATTERN RECOGNITION,139.

Yin，Wanguang,et al."Discriminative subspace learning via optimization on Riemannian manifold".PATTERN RECOGNITION 139(2023).