Modified Hebbian learning for curve and surface fitting
Neural Networks
Adaptive eigenvalue decomposition algorithm for real time acoustic source localization system
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
A modified MCA EXIN algorithm and its convergence analysis
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Orthogonal eigensubspace estimation using neural networks
IEEE Transactions on Signal Processing
Total least mean squares algorithm
IEEE Transactions on Signal Processing
Development and analysis of a neural network approach toPisarenko's harmonic retrieval method
IEEE Transactions on Signal Processing
Convergence analysis of a deterministic discrete time system of feng's MCA learning algorithm
IEEE Transactions on Signal Processing
The MCA EXIN neuron for the minor component analysis
IEEE Transactions on Neural Networks
On the discrete-time dynamics of the basic Hebbian neural network node
IEEE Transactions on Neural Networks
Convergence analysis of a deterministic discrete time system of Oja's PCA learning algorithm
IEEE Transactions on Neural Networks
A unified learning algorithm to extract principal and minor components
Digital Signal Processing
A Self-Stabilizing Neural Algorithm for Total Least Squares Filtering
Neural Processing Letters
A self-stabilizing MSA algorithm in high-dimension data stream
Neural Networks
Hi-index | 0.01 |
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.