It shows a minimum for the regularization parameter 1.58.
24, 25 T 2 spectra were regularized using a minimum curvature constraint with a conservative regularization held constant across all voxels.
MiniDisCo algorithm makes the assumption of minimum spectral dispersion for NMF regularization [16], and MDMD-NMF regularizes with minimum spectral dispersion and maximum spatial dispersion [17].
This paper describes an improved approach, which uses the ℓ0−norm regularization algorithm with the minimum entropy based deconvolution, which gives the benefits of faster convergence of algorithms and increase robustness to additive noise and inverse filter length.
This implies that the model we chose is optimal not only from the viewpoint of minimum ABIC, but also for parameter regularization for an ill-posed discrete linear problem (Hansen, 1992).
Minimum relative entropy (MRE) and Tikhonov regularization (TR) were compared by Neupauer et al. [Water Resour. Res. 36 (2000 2469]] on the basis of an example plume source reconstruction problem originally proposed by Skaggs and Kabala [Water Resour. Res. 30 (1994) 71] and a boxcar-like function.
By exploiting the idea of combining the statistical minimum risk estimation paradigm with numerical descriptive regularization techniques, we address a new fused statistical descriptive regularization (SDR) strategy for enhanced radar imaging.
According to Fig. 6, the best regularization parameter should be selected for minimum total variance.
This paper describes a novel approach that represents feature selection as a continuous regularization problem which has a single, global minimum, where the model's complexity is measured using a 1-norm on the parameter vector.
In order to obtain the best least-squares solution, the regularization method is used to seek the minimum value of (left| {Jf - eta } right|^{2},+,alpha^{2} left| f right|^{2}).
On the other hand, Xu [15] proposed that regularization can be used to find the minimum-norm solution of the minimization problem.
