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In order to avoid overfitting, we also enforce regularization on the model parameters.
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In the first extension, we use group-sparsity enforcing regularization term to impose the sparse structure.
The innovative idea of this paper is to aggregate the DEDR-optimal fixed-point iterative reconstruction/enhancement procedures developed in the previous studies [7, 8, 10] with the multi-level robustness and convergence enforcing regularization via constructing the proper projections onto convex sets (POCS) in the solution domain.
Functions Φ are chosen in such a way to enforce a global regularization constraint on the cross-spectra.
Here, the regularization term R : X → [ 0, + ∞ ] is intended to enforce certain regularity properties of the approximate solution and to stabilize the process of solving (1).
Firstly, a generic spatio-temporal regularization term is designed and used together with the standard ℓ1 regularization term to enforce a sparse decomposition preserving the spatio-temporal structure of the signal.
Furthermore, the problem (32b) could be modified using regularization [72] to enforce sparsity in ({bar {K}}_{k}).
with λ a regularization parameter to enforce smoothness of g z) (see [28, 31]), and with є a small positive number.
The idea of regularization
In this paper, we extend the Markov model to include regularization terms that enforce sparseness of the inferred gene network and allow incorporation of prior network information.
To enforce the network generalization ability, regularization was used for training, since this is not naturally achieved through the learning algorithm.
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