Reweighted nuclear norm regularization: A SPARSEVA approach
The aim of this paper is to develop a method to estimate high order FIR and ARX models
using least squares with re-weighted nuclear norm regularization. Typically, the choice of
the tuning parameter in the reweighting scheme is computationally expensive, hence we
propose the use of the SPARSEVA (SPARSe Estimation based on a VAlidation criterion)
framework to overcome this problem. Furthermore, we suggest the use of the prediction error
criterion (PEC) to select the tuning parameter in the SPARSEVA algorithm. Numerical …
using least squares with re-weighted nuclear norm regularization. Typically, the choice of
the tuning parameter in the reweighting scheme is computationally expensive, hence we
propose the use of the SPARSEVA (SPARSe Estimation based on a VAlidation criterion)
framework to overcome this problem. Furthermore, we suggest the use of the prediction error
criterion (PEC) to select the tuning parameter in the SPARSEVA algorithm. Numerical …
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