A neural networks learning algorithm for minor component analysis and its convergence analysis

作者：Peng, Dezhong^*; Yi, Zhang; Lv, JianCheng; Xiang, Yong

来源：Neurocomputing, 2008, 71(7-9): 1748-1752.

DOI：10.1016/j.neucom.2007.11.012

• minor component analysis (MCA)
• deterministic discrete time (DDT) system
• eigenvalue
• eigenvector

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.

• 出版日期2008-3
• 单位迪肯大学; 电子科技大学