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
Orthogonal eigensubspace estimation using neural networks
IEEE Transactions on Signal Processing
On the inflation method in adaptive noise-subspace estimator
IEEE Transactions on Signal Processing
Total least mean squares algorithm
IEEE Transactions on Signal Processing
Adaptive estimation of eigensubspace
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
Against the convergence of the minor component analysis neurons
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
A Class of Self-Stabilizing MCA Learning Algorithms
IEEE Transactions on Neural Networks
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Minor subspace analysis (MSA) is a statistical method for extracting the subspace spanned by all the eigenvectors associated with the minor eigenvalues of the autocorrelation matrix of a high-dimension vector sequence. In this paper, we propose a self-stabilizing neural network learning algorithm for tracking minor subspace in high-dimension data stream. Dynamics of the proposed algorithm are analyzed via a corresponding deterministic continuous time (DCT) system and stochastic discrete time (SDT) system methods. The proposed algorithm provides an efficient online learning for tracking the MS and can track an orthonormal basis of the MS. Computer simulations are carried out to confirm the theoretical results.