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
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Subspace tracking of fast time-varying channels in precoded MIMO-OFDM systems
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
DOA Estimation of Multiple Convolutively Mixed Sources Based on Principle Component Analysis
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
An Automatic Colon Cleansing Method for Virtual Colonoscopy
ICISE '09 Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Fast adaptive algorithms for minor component analysis using Householder transformation
Digital Signal Processing
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Fast principal component extraction by a weighted informationcriterion
IEEE Transactions on Signal Processing
Fast and Stable Subspace Tracking
IEEE Transactions on Signal Processing
Algorithms for accelerated convergence of adaptive PCA
IEEE Transactions on Neural Networks
A Class of Self-Stabilizing MCA Learning Algorithms
IEEE Transactions on Neural Networks
IEEE Transactions on Signal Processing
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This article introduces new low cost algorithms for the adaptive estimation and tracking of principal and minor components. The proposed algorithms are based on the well-known OPAST method which is adapted and extended in order to achieve the desired MCA or PCA (Minor or Principal Component Analysis). For the PCA case, we propose efficient solutions using Givens rotations to estimate the principal components out of the weight matrix given by OPAST method. These solutions are then extended to the MCA case by using a transformed data covariance matrix in such a way the desired minor components are obtained from the PCA of the new (transformed) matrix. Finally, as a byproduct of our PCA algorithm, we propose a fast adaptive algorithm for data whitening that is shown to overcome the recently proposed RLS-based whitening method.