This result indicates that, in general, superior results are obtained by directly considering motion information between neighboring cells in the smoothness term, which cannot be achieved with the usual pixel-wise regularization approaches.
Neighborhood alignment is performed by adding piece-wise smooth regularization constraints to an energy function.
We propose a convex variational approach to compute localized density matrices for both zero temperature and finite temperature cases, by adding an entry-wise ℓ1 regularization to the free energy of the quantum system.
Element-wise l1 norm regularization (known as lasso) encourages the eigenphone matrix to be sparse, which reduces the number of effective free parameters and improves generalization.
Column-wise unsquared l2 norm regularization (known as group lasso) acts like the lasso at the column level, encouraging column sparsity in the eigenphone matrix, i.e., preferring an eigenphone matrix with many zero columns as solution.
The column-wise unsquared l2 norm regularization (group lasso) forces many columns of the eigenphone matrix to be zero, thus preventing the dimension of the phone variation subspace from being higher than necessary.
The column-wise unsquared l2 norm regularization forces some columns of the matrix to be zero, thus effectively preventing the dimensionality of the phone variation subspace to grow beyond what is necessary.
The advantages of both the l1 regularization and the column-wise l2 regularization combine well.
From Table5, it can be seen that when the weighting factor λ3 is set to 20∼30, the recognition results obtained by applying l1 regularization and the column-wise l2 regularization simultaneously are better than that of using any one of the regularizers.
In this study, we propose to integrate pair-wise distance metric learning as the regularization of model parameter optimization.
Squared l2 norm regularization promotes an element-wise shrinkage of the estimated matrix towards zero, thus alleviating over-fitting.
