A Class of Self-Stabilizing MCA Learning Algorithms

Authors:
Mao Ye;Xu-Qian Fan;Xue Li
Affiliations:
Sch. of Comput. Sci. & Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2006

Citing 0
Cited 6

A Self-Stabilizing Neural Algorithm for Total Least Squares Filtering

Neural Processing Letters
On the discrete-time dynamics of a class of self-stabilizing MCA extraction algorithms

IEEE Transactions on Neural Networks
A self-stabilizing MSA algorithm in high-dimension data stream

Neural Networks
Low complexity adaptive algorithms for Principal and Minor Component Analysis

Digital Signal Processing
Convergence analysis of deterministic discrete time system of a unified self-stabilizing algorithm for PCA and MCA

Neural Networks
Full Length Article: A hybrid model through the fusion of type-2 fuzzy logic systems and extreme learning machines for modelling permeability prediction

Information Fusion

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this letter, we propose a class of self-stabilizing learning algorithms for minor component analysis (MCA), which includes a few well-known MCA learning algorithms. Self-stabilizing means that the sign of the weight vector length change is independent of the presented input vector. For these algorithms, rigorous global convergence proof is given and the convergence rate is also discussed. By combining the positive properties of these algorithms, a new learning algorithm is proposed which can improve the performance. Simulations are employed to confirm our theoretical results