An Adaptive Self-Stabilizing Algorithm for Minor Generalized Eigenvector Extraction and Its Convergence Analysis.
Clicks: 285
ID: 56012
2018
Generalized eigendecomposition, which extracts the generalized eigenvector from a matrix pencil, is a powerful tool and has been widely used in many fields, such as data classification and blind source separation. First, to extract the minor generalized eigenvector (MGE), we propose a deterministic discrete-time (DDT) system. Unlike some existing systems, the proposed DDT system does not need to normalize the weight vector in each iteration, since the weight vectors in the proposed DDT system are self-stabilizing. Second, we propose an adaptive algorithm corresponding to the proposed DDT system. Moreover, we study the dynamic behavior and convergence properties of the proposed DDT system and prove that the weight vector must converge to the direction of the MGE of a matrix pencil under some mild conditions. Numerical simulations show that the proposed algorithm has a better performance in terms of convergence speed and estimation accuracy than some existing algorithms. Finally, we conduct two experiments on real data sets to demonstrate its practicability.
| Reference Key |
yingbin2018anieee
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Yingbin, Gao;Xiangyu, Kong;Zhengxin, Zhang;Li'an, Hou; |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Year | 2018 |
| DOI | 10.1109/TNNLS.2017.2783360 |
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.