Toeplitz equations by conjugate gradients with circulant preconditioner
SIAM Journal on Scientific and Statistical Computing
Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Matrix computations (3rd ed.)
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Natural gradient works efficiently in learning
Neural Computation
Acoustic signal processing for telecommunication
Acoustic signal processing for telecommunication
Prior knowledge and preferential structures in gradient descent learning algorithms
The Journal of Machine Learning Research
Acoustic MIMO Signal Processing (Signals and Communication Technology)
Acoustic MIMO Signal Processing (Signals and Communication Technology)
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Advances in Network and Acoustic Echo Cancellation
Advances in Network and Acoustic Echo Cancellation
Gradient-based variable forgetting factor RLS algorithm in time-varying environments
IEEE Transactions on Signal Processing - Part II
Adaptive tracking of linear time-variant systems by extended RLSalgorithms
IEEE Transactions on Signal Processing
Exploiting sparsity in adaptive filters
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
LMS estimation via structural detection
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Space-time adaptive reduced-rank multistage Wiener filtering for asynchronous DS-CDMA
IEEE Transactions on Signal Processing
Analysis of conjugate gradient algorithms for adaptive filtering
IEEE Transactions on Signal Processing
H∞ optimality of the LMS algorithm
IEEE Transactions on Signal Processing
A robust variable step-size LMS-type algorithm: analysis andsimulations
IEEE Transactions on Signal Processing
An efficient robust adaptive filtering algorithm based on parallelsubgradient projection techniques
IEEE Transactions on Signal Processing
Convergence of exponentiated gradient algorithms
IEEE Transactions on Signal Processing
A class of adaptive step-size control algorithms for adaptivefilters
IEEE Transactions on Signal Processing
Adaptive Parallel Quadratic-Metric Projection Algorithms
IEEE Transactions on Audio, Speech, and Language Processing
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
Performance of reduced-rank linear interference suppression
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Robust reduced-rank adaptive algorithm based on parallel subgradient projection and Krylov subspace
IEEE Transactions on Signal Processing
A unified view of adaptive variable-metric projection algorithms
EURASIP Journal on Advances in Signal Processing
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This paper proposes a novel adaptive filtering scheme named the Krylov-proportionate normalized least-mean-square (KPNLMS) algorithm. KPNLMS exploits the benefits (i.e., fast convergence for sparse unknown systems) of the proportionate NLMS algorithm, but its applications are not limited to sparse unknown systems. A set of orthonormal basis vectors is generated from a certain Krylov sequence. It is proven that the unknown system is sparse with respect to the basis vectors in case of fairly uncorrelated input data. Different adaptation gain is allocated to a coefficient ot each basis vector, and the gain is roughly proportional to the absolute value of the corresponding coefficient of the current estimate. KPNLMS enjoys i) fast convergence, ii) linear complexity per iteration, and iii) no use of any a priori information. Numerical examples demonstrate significant advantages of the proposed scheme over the reduced-rank method based on the multistag Wiener filter (MWF) and the transform-domain adaptive filter (TDAF) both in noisy and silent situations.