A multiple graph ensemble regularization framework is proposed to learn the optimal intrinsic manifold.
To determine how much variance in the MEG signals was captured by each of the 3 models, we calculated each model's R using multiple linear regression (no regularization) at each sensor and for each time point independently.
Determining the proper regularization parameter requires multiple solutions of the regularized optimization problem (7), therefore the computational efficiency is also crucial.
It might be possible to remedy some of these ill-posed problems with a regularization algorithm for multiple linear regression and through redesigning the algorithm with the regularized solution.
We have also outlined basic procedures for large-scale application that take into account issues of regularization, computational burden, and multiple testing.
To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization.
Second, we apply a refinement of the Multi-Task Sparse Learning (MTSL) framework to exploit the relationships among multiple shared tasks generated by changing the regularization parameter during the recognition stage.
Model parameters were obtained using quantitative structure property relationships (QSPRs) developed using multiple linear regression and artificial neural networks with Bayesian regularization.
In [20], a deformable spatial pyramid model is proposed in a regularization framework for estimating dense disparities using multiple SIFT features.
