Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409175 | Neurocomputing | 2008 | 5 Pages |
Abstract
The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Dezhong Peng, Zhang Yi, JianCheng Lv, Yong Xiang,