Sparsity regularized recursive total least-squares

Tanc, A. Korhan

doi:10.1016/j.dsp.2015.02.018

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…These algorithms recursively calculate and track the eigenvector corresponding to the minimum eigenvalue (the TLS solution) from the inverse covariance matrix of the augmented sample matrix. Some TLS based algorithms were proposed for the sparse signal processing (Tanc 2015 ; Arablouei 2016 ; Zhu et al. 2011 ; Dumitrescu 2013 ; Lim and Pang 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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$$\ell _1$$ ℓ 1 -regularized recursive total least squares based sparse system identification for the error-in-variables

Lim

Pang

2016

SpringerPlus

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In this paper an -regularized recursive total least squares (RTLS) algorithm is considered for the sparse system identification. Although recursive least squares (RLS) has been successfully applied in sparse system identification, the estimation performance in RLS based algorithms becomes worse, when both input and output are contaminated by noise (the error-in-variables problem). We proposed an algorithm to handle the error-in-variables problem. The proposed -RTLS algorithm is an RLS like iteration using the regularization. The proposed algorithm not only gives excellent performance but also reduces the required complexity through the effective inversion matrix handling. Simulations demonstrate the superiority of the proposed -regularized RTLS for the sparse system identification setting.

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Section: Introductionmentioning

confidence: 99%

“… 2011 ; Dumitrescu 2013 ; Lim and Pang 2016 ). The algorithms in Tanc ( 2015 ), Arablouei ( 2016 ) utilized the gradient based method. The algorithms in Zhu et al.…”

Section: Introductionmentioning

confidence: 99%