VLSI array processors
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Matrix computations (3rd ed.)
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
An adaptive quasi-Newton algorithm for eigensubspace estimation
IEEE Transactions on Signal Processing
Adaptive estimation of eigensubspace
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Conjugate gradient eigenstructure tracking for adaptive spectralestimation
IEEE Transactions on Signal Processing
Fast recursive low-rank linear prediction frequency estimationalgorithms
IEEE Transactions on Signal Processing
Fast principal component extraction by a weighted informationcriterion
IEEE Transactions on Signal Processing
Noniterative subspace tracking
IEEE Transactions on Signal Processing
Adaptive minor component extraction with modular structure
IEEE Transactions on Signal Processing
An updating algorithm for subspace tracking
IEEE Transactions on Signal Processing
Blind multiuser detection: a subspace approach
IEEE Transactions on Information Theory
Robust recursive least squares learning algorithm for principal component analysis
IEEE Transactions on Neural Networks
Principal component extraction using recursive least squares learning
IEEE Transactions on Neural Networks
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This paper describes an adaptive eigenanalysis algorithm for estimating the eigenstructure of a sample covariance matrix. A minimization nonquadratic criterion is formulated by exploring the relationship of eigenvalues between the covariance matrix and its inverse matrix. A quasi-Newton approach is proposed to perform the task of minimization. It is shown that the new algorithm can be acted as another power method but without square-root operation. This approach is well-suited to parallel implementation when it is used for iterative estimation of multiple principal components by a deflation method. A unified modular architecture is developed for the analysis of both principal and minor components. Simulation experiments are carried out with both stationary and nonstationary data. The results show that the proposed method is capable of extracting multiple principal components in parallel with fast convergence speed and high tracking accuracy.