The ideal variable selection procedure would search for the best subset of predictors, which is equivalent to imposing an l(0)-penalty on the regression coefficients. Since this optimization is a nondeterministic polynomial-time hard (NP-hard) problem that does not scale with number o...
Meanwhile, in the cost function they introduce a penalty term that favours sparsity to enable the applicability for sparse condition. Moreover, the l0-SH-WL-LMS algorithm also makes full use of the non-circular properties of the signals of interest to improve the tracking capability and ...
R. Analysis of total variation penalty methods. Inverse Prob. 10(6), 1217–1229 (1994). 17. Zhang, F., Dai, R. & Liu, H. Seismic inversion based on L1-norm misfit function and total variation regularization. J. Appl. Geophys. 109, 111–118 (2014). 18. Gholami, A. Nonlinear ...
But L2-norm penalty function inherently leads to considerable smoothing of the solution, which reduces the accuracy of distinguishing abnormalities and locating diseased regions. Directly using L1-norm penalty function, however, may greatly increase the computational complexity due to its non-...
The criterion is based on the representation of the sum of the normalized residuals and regularization terms as a function of the penalty parameter. Reliability of the results is improved and broader applicability of the approach is now possible.doi:10.1002/cem.3227...
We propose a numerical procedure for solving large scale regression estimation problems involving a structured l0-norm penalty function. This numerical procedure blends the ideas of randomization, blockwise coordinate descent algorithms, and a closed-form representation of the proximal operator of the ...
But L2-norm penalty function inherently leads to considerable smoothing of the solution, which reduces the accuracy of distinguishing abnormalities and locating diseased regions. Directly using L1-norm penalty function, however, may greatly increase the computational complexity due ...
But L2-norm penalty function inherently leads to considerable smoothing of the solution, which reduces the accuracy of distinguishing abnormalities and locating diseased regions. Directly using L1-norm penalty function, however, may greatly increase the computational complexity due to its non-...
Zero-attracting penaltyLeast mean squareThe l 0 -norm-constraint algorithm is widely used in sparse system identification due to its attractive performance. However, the algorithm is sensitive to the tuning parameters and its convergence speed can be further improved due to the small attraction range...
An additional \\(l_0\\) norm penalty is integrated into the objective function of the signal variance vector to achieve zero attraction. With a few user-adjusted parameters, the proposed strategy achieves a faster implementation while inheriting the superior performance of the SBL method for DOA...