Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
The propagator method for source bearing estimation
Signal Processing
A new adaptive algorithm for minor component analysis
Signal Processing
On a Class of Orthonormal Algorithms for Principal and Minor Subspace Tracking
Journal of VLSI Signal Processing Systems
Fast adaptive eigenvalue decomposition: a maximum likelihood approach
Signal Processing
Asymptotic performance analysis of PCA algorithms based on the weighted subspace criterion
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Fast approximated power iteration subspace tracking
IEEE Transactions on Signal Processing - Part I
Adaptive minor component extraction with modular structure
IEEE Transactions on Signal Processing
Performance analysis of an adaptive algorithm for tracking dominantsubspaces
IEEE Transactions on Signal Processing
Low complexity adaptive algorithms for Principal and Minor Component Analysis
Digital Signal Processing
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In this paper, we propose new adaptive algorithms for the extraction and tracking of the least (minor) or eventually, principal eigenvectors of a positive Hermitian covariance matrix. The main advantage of our proposed algorithms is their low computational complexity and numerical stability even in the minor component analysis case. The proposed algorithms are considered fast in the sense that their computational cost is O(np) flops per iteration where n is the size of the observation vector and p