Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Vector quantization and signal compression
Vector quantization and signal compression
A singular value decomposition updating algorithm for subspace tracking
SIAM Journal on Matrix Analysis and Applications
Matrix computations (3rd ed.)
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03
A new family of EVD tracking algorithms using Givens rotations
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
A subspace method for the blind identification of multiple time-varying FIR channels
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A novel adaptive eigendecomposition technique with application to automatic target recognition
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Fast adaptive algorithms for minor component analysis using Householder transformation
Digital Signal Processing
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In this paper, we address the problem of adaptive eigenvalue decomposition (EVD). We propose a new approach, based on the optimization of the log-likelihood criterion. The parameters of the log-likelihood to be estimated are the eigenvectors and the eigenvalues of the data covariance matrix. They are actualized by means of a stochastic algorithm that requires little computational cost. Furthermore, the particular structure of the algorithm, that we named MALASE, ensures the orthonormality of the estimated basis of eigenvectors at each step of the algorithm. MALASE algorithm shows strong links with many Givens rotation based update algorithms that we discuss. We consider convergence issues for MALASE algorithm and give the expression of the asymptotic covariance matrix of the estimated parameters. The practical interest of the proposed method is shown on examples.