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This motivates new regularization methods [9, 10, 24, 54, 55].
We then establish the relationship between the noise level γ>0, the parameter of regularization α>0, and the truncation (or cut-off) parameter K.
Most of models focus on the regularizations as length regularization [3], mean curvature regularization [22], and H1 regularization [26].
Such a process is called regularization [14].
MaxEntL1 ∙ (multinomial logistic regression with L1 regularization) [27].
In the image PR field, the image regularization, such as l 1 regularization [13, 14], is focused by researchers.
Evgeniou et al. introduced an approach that uses graph-based regularization [29, 30].
The early approaches used least square (LS) [21] and Tikhonov regularization [22] as priors.
To enforce this spatial smoothness, we utilize the spatial regularization [62] in MFF.
Then manifold regularization [44] is applied with a second linear regression model.
Bayesian learning is beneficial to deal with sparse representation [9] and model regularization [7].
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