Greedy Sparse RLS

Dumitrescu, Bogdan; Onose, Alexandru; Helin, Petri; Tăbuş, Ioan

doi:10.1109/tsp.2012.2187285

Cited by 38 publications

(19 citation statements)

References 22 publications

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“…Image restoration as a solution of the ill-posed inverse problem typically concentrates on content models of sparse, locally supported signal segments or components in a space-scale varying context. Sometimes instead of an equal access to the whole sensed data to compute a user-defined solution, only locally defined data, predefined geometric component or the like are taken with higher weight to estimate an adaptively and locally sparse solution [30]. Moreover, adaptive regularization is applied with family of priors taking into account the geometric properties of the image [31] or corresponding to the ROI preselected by sensing procedure [32].…”

Section: Framework Of the Proposed Methodsmentioning

confidence: 99%

Recovery of CT stroke hypodensity – An adaptive variational approach

Przelaskowski

2015

Computerized Medical Imaging and Graphics

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Section: Framework Of the Proposed Methodsmentioning

confidence: 99%

Recovery of CT stroke hypodensity – An adaptive variational approach

Przelaskowski

2015

Computerized Medical Imaging and Graphics

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“…The so-called SPARLS algorithm is introduced in [14] using an expectation-maximization (EM) approach. The work of [15] proposes an adaptive version of the greedy least squares method using partial orthogonalization to systems. The work of [16] modifies the RLS algorithm by using a general convex function of the system parameters, resulting in the l 0 -RLS and l 1 -RLS algorithms.…”

Section: Introductionmentioning

confidence: 99%

Zero Attracting Recursive Least Squares Algorithms

Hong

Gao

Chen

2016

IEEE Trans. Veh. Technol.

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Abstract-The l1-norm sparsity constraint is a widely used technique for constructing sparse models. In this contribution, two zero-attracting recursive least squares algorithms, referred to as ZA-RLS-I and ZA-RLS-II, are derived by employing the l1-norm of parameter vector constraint to facilitate the model sparsity. In order to achieve a closed-form solution, the l1-norm of the parameter vector is approximated by an adaptively weighted l2-norm, in which the weighting factors are set as the inversion of the associated l1-norm of parameter estimates that are readily available in the adaptive learning environment. ZA-RLS-II is computationally more efficient than ZA-RLS-I by exploiting the known results from linear algebra as well as the sparsity of the system. The proposed algorithms are proven to converge, and adaptive sparse channel estimation is used to demonstrate the effectiveness of the proposed approach.Index Terms-Zero attracting recursive least squares algorithm, adaptive channel estimation, sparse model, l1-norm

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“…These algorithms are particularly useful for sparse system identification and sparse channel parameter estimation [9,10]. Well-known examples include the zero-attracting least mean square (ZA-LMS) [3], reweighted ZA-LMS (RZA-LMS) [3], and sparse recursive least mean [8]. Many variants of sparse adaptive filtering algorithms have also been developed in [11][12][13]19,26,27].…”

Section: Introductionmentioning

confidence: 99%

Sparse Least Logarithmic Absolute Difference Algorithm with Correntropy-Induced Metric Penalty

Chen

Zhao

et al. 2015

Circuits Syst Signal Process

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Sparse adaptive filtering algorithms are utilized to exploit system sparsity as well as to mitigate interferences in many applications such as channel estimation and system identification. In order to improve the robustness of the sparse adaptive filtering, a novel adaptive filter is developed in this work by incorporating a correntropy-induced metric (CIM) constraint into the least logarithmic absolute difference (LLAD) algorithm. The CIM as an l 0 -norm approximation exerts a zero attraction, and hence, the B Badong Chen

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Greedy Sparse RLS

Cited by 38 publications

References 22 publications

Recovery of CT stroke hypodensity – An adaptive variational approach

Recovery of CT stroke hypodensity – An adaptive variational approach

Zero Attracting Recursive Least Squares Algorithms

Sparse Least Logarithmic Absolute Difference Algorithm with Correntropy-Induced Metric Penalty

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