Modified Hebbian learning for curve and surface fitting
Neural Networks
A Generalized Learning Algorithm of Minor Component
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
A stable MCA learning algorithm
Computers & Mathematics with Applications
Total least mean squares algorithm
IEEE Transactions on Signal Processing
Adaptive minor component extraction with modular structure
IEEE Transactions on Signal Processing
On the discrete time dynamics of a self-stabilizing MCA learning algorithm
Mathematical and Computer Modelling: An International Journal
A minor subspace analysis algorithm
IEEE Transactions on Neural Networks
A class of learning algorithms for principal component analysis and minor component analysis
IEEE Transactions on Neural Networks
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
Neural network learning algorithms for tracking minor subspace in high-dimensional data stream
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 Class of Self-Stabilizing MCA Learning Algorithms
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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The minor component analysis (MCA) deals with the recovery of the eigenvector associated to the smallest eigenvalue of the autocorrelation matrix of the input dada, and it is a very important tool for signal processing and data analysis. This brief analyzes the convergence and stability of a class of self-stabilizing MCA algorithms via a deterministic discrete-time (DDT) method. Some sufficient conditions are obtained to guarantee the convergence of these learning algorithms. Simulations are carried out to further illustrate the theoretical results achieved. It can be concluded that these self-stabilizing algorithms can efficiently extract the minor component (MC), and they outperform some existing MCA methods.