Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Riemannian gradient-based Zhang neural network method for linear discriminant analysis. |
| Συγγραφείς: |
Wang, Yu-Hang, Xie, Zhuo-Cheng, Fu, Meng-Zhen, Ren, Huan |
| Πηγή: |
AIMS Mathematics; 2026, Vol. 11 Issue 8, p1-21, 21p |
| Θεματικοί όροι: |
Classification algorithms, Artificial neural networks, Fisher discriminant analysis, Dimensional reduction algorithms, Human facial recognition software, Stability theory |
| Περίληψη: |
Linear discriminant analysis (LDA) is a classic statistical method employed for classification and dimensionality reduction of large-scale datasets. In this paper, we propose a Zhang neural network method based on Riemannian gradient. Compared with traditional approaches for solving LDA, the proposed method achieves higher convergence accuracy, accelerated convergence speed, and reduced parameter sensitivity. In terms of theoretical analysis, we have investigated the properties and geometric dynamic characteristics, and provided stability analysis of the algorithm. Finally, through experiments with synthetic data and ORL face images, and by making a clear comparison with WH2, the advantages of the proposed algorithm were verified. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |