Modified Hebbian learning for curve and surface fitting
Neural Networks
A unified learning algorithm to extract principal and minor components
Digital Signal Processing
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
Total least mean squares algorithm
IEEE Transactions on Signal Processing
Adaptive Principal component EXtraction (APEX) and applications
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
Robust recursive least squares learning algorithm for principal component analysis
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
A general backpropagation algorithm for feedforward neural networks learning
IEEE Transactions on Neural Networks
Coupled principal component analysis
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
Principal component extraction using recursive least squares learning
IEEE Transactions on Neural Networks
A Dual Purpose Principal and Minor Subspace Gradient Flow
IEEE Transactions on Signal Processing
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Unified algorithms for principal and minor components analysis can be used to extract principal components and if altered simply by the sign, it can also serve as a minor component extractor. Obviously, the convergence of these algorithms is an essential issue in practical applications. This paper studies the convergence of a unified PCA and MCA algorithm via a corresponding deterministic discrete-time (DDT) system and some sufficient conditions to guarantee convergence are obtained. Simulations are carried out to further illustrate the theoretical results achieved.