Bibliographic Details
| Title: |
Sparse Online Principal Component for Elliptical Factor Models. |
| Authors: |
Zhou, Yuanyuan1 zhouyy0611@163.com, Liu, Siqi2 liusiqi20240130@163.com, Guo, Guangbao3 ggb11111111@163.com |
| Source: |
IAENG International Journal of Computer Science. Jul2026, Vol. 53 Issue 7, p2640-2644. 5p. |
| Subjects: |
Principal components analysis, Real-time computing, Parameter estimation, Scalability, Sequential learning |
| Abstract: |
This paper develops an elliptical factor model that extends traditional factor models to better handle heavytailed data. Parameter estimation employs sparse principal component, which uses penalty terms to induce sparsity in the loading matrix, and a sparse online principal component method for real-time updates. The proposed sparse online principal component framework allows sequential updating of sparse principal components without storing historical data. Simulations confirm the method achieves superior accuracy and sparsity compared to existing approaches. The method is robust and scalable for heavy-tailed datasets. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
