Sparsity regularised recursive least squares adaptive filtering

Ekşioğlu, Ender M.

doi:10.1049/iet-spr.2010.0083

Cited by 57 publications

(35 citation statements)

References 11 publications

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“…We name the resulting algorithms as 1 -RTLS and 0 -RTLS when 1 norm and approximate 0 norm penalty functions are employed, respectively. Similar to the sparsity regularized RLS algorithms [3,4], our algorithms do not require heavy matrix calculations, and they have O(L 2 ) operational complexity per time instant. Furthermore, it can be deduced that our algorithms rely on first order approximation, and hence they are Algorithm 1 Sparsity regularized RTLS procedure.…”

Section: Sparsity Regularized Rtlsmentioning

confidence: 97%

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Sparsity regularized recursive total least-squares

Tanc

2015

Digital Signal Processing

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Section: Sparsity Regularized Rtlsmentioning

confidence: 97%

“…In [2], the authors introduced SPARLS algorithm which relies on 1 norm regularization and expectation-maximization (EM). RLS algorithms regularized with weighted 1 norm and approximate 0 norm were presented in [3] and [4], respectively. Greedy sparse RLS algorithm based on optimized orthogonal matching pursuit (OMP) was suggested in [5].…”

Section: Introductionmentioning

confidence: 99%

Sparsity regularized recursive total least-squares

Tanc

2015

Digital Signal Processing

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“…In this case, the proposed algorithm is called CIMRGMCC.Remark From Table , we know that the ZARGMCC and CIMRGMCC will reduce to ZARLS and CIMRLS when f ( e ( n )) = 1 and p = 2. While they will reduce to RMCC when ρ = 0 and p = 2.…”

Section: Sparse Recursive Generalized Maximum Correntropy Criterion Amentioning

confidence: 99%

Recursive Generalized Maximum Correntropy Criterion Algorithm with Sparse Penalty Constraints for System Identification

Duan

Chen

et al. 2016

Asian Journal of Control

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To address the sparse system identification problem in a non‐Gaussian impulsive noise environment, the recursive generalized maximum correntropy criterion (RGMCC) algorithm with sparse penalty constraints is proposed to combat impulsive‐inducing instability. Specifically, a recursive algorithm based on the generalized correntropy with a forgetting factor of error is developed to improve the performance of the sparsity aware maximum correntropy criterion algorithms by achieving a robust steady‐state error. Considering an unknown sparse system, the l1‐norm and correntropy induced metric are employed in the RGMCC algorithm to exploit sparsity as well as to mitigate impulsive noise simultaneously. Numerical simulations are given to show that the proposed algorithm is robust while providing robust steady‐state estimation performance.

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“…Simulation results confirm the efficiency of the proposed algorithm. Note that in the literature the sparsity inducing penalty terms have been successfully used in linear adaptive filtering algorithms, such as least mean square (LMS) and recursive least squares (RLS) [11][12][13][14][15][16][17]. These sparsity regularized algorithms are shown to be highly efficient for sparse signal estimation or system identification.…”

Section: Introductionmentioning

confidence: 99%

Sparse kernel recursive least squares using L<inf>1</inf> regularization and a fixed-point sub-iteration

Chen

Zheng

Prı́ncipe

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A new kernel adaptive filtering (KAF) algorithm, namely the sparse kernel recursive least squares (SKRLS), is derived by adding a l 1 -norm penalty on the center coefficients to the least squares (LS) cost (i.e. the sum of the squared errors). In each iteration, the center coefficients are updated by a fixed-point sub-iteration. Compared with the original KRLS algorithm, the proposed algorithm can produce a much sparser network, in which many coefficients are negligibly small. A much more compact structure can thus be achieved by pruning these negligible centers. Simulation results show that the SKRLS performs very well, yielding a very sparse network while preserving a desirable performance.

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Sparsity regularised recursive least squares adaptive filtering

Cited by 57 publications

References 11 publications

Sparsity regularized recursive total least-squares

Sparsity regularized recursive total least-squares

Recursive Generalized Maximum Correntropy Criterion Algorithm with Sparse Penalty Constraints for System Identification

Sparse kernel recursive least squares using L<inf>1</inf> regularization and a fixed-point sub-iteration

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