Convergence Analysis of Möller Algorithm for Estimating Minor Component

The minor component analysis (MCA) deals with the recovery of the eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of the input data, and Möller algorithm is a famous self-stability MCA method. In this paper, we present a convergence analysis of Möller algorithm for...

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Bibliographic Details
Published in:Neural processing letters 2015-10, Vol.42 (2), p.355-368
Main Authors: Gao, Yingbin, Kong, Xiangyu, Hu, Changhua, Zhang, Huihui, Hou, Li’an
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Complex Systems
Computational Intelligence
Computer Science
Convergence
Eigenvalues
Eigenvectors
Expected values
Experiments
Simulation
Stability analysis
Telematics
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Summary:The minor component analysis (MCA) deals with the recovery of the eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of the input data, and Möller algorithm is a famous self-stability MCA method. In this paper, we present a convergence analysis of Möller algorithm for estimating minor component of an input signal via a deterministic discrete time method. Some sufficient conditions are obtained to guarantee the convergence of Möller algorithm. Simulations are carried out to further illustrate the theoretical results achieved.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-014-9360-y