Updater for L1 regularized problems.
R(w) = ||w||_1
Uses a step-size decreasing with the square root of the number of iterations.
Instead of subgradient of the regularizer, the proximal operator for the
L1 regularization is applied after the gradient step. This is known to
result in better sparsity of the intermediate solution.
The corresponding proximal operator for the L1 norm is the soft-thresholding
function. That is, each weight component is shrunk towards 0 by shrinkageVal.
If w > shrinkageVal, set weight component to w-shrinkageVal.
If w < -shrinkageVal, set weight component to w+shrinkageVal.
If -shrinkageVal < w < shrinkageVal, set weight component to 0.
Equivalently, set weight component to signum(w) * max(0.0, abs(w) - shrinkageVal)
Linear Supertypes
Updater, BasicUpdater[DenseVector[Double]], Serializable, Serializable, AnyRef, Any
Updater for L1 regularized problems. R(w) = ||w||_1 Uses a step-size decreasing with the square root of the number of iterations.
Instead of subgradient of the regularizer, the proximal operator for the L1 regularization is applied after the gradient step. This is known to result in better sparsity of the intermediate solution.
The corresponding proximal operator for the L1 norm is the soft-thresholding function. That is, each weight component is shrunk towards 0 by shrinkageVal.
If w > shrinkageVal, set weight component to w-shrinkageVal. If w < -shrinkageVal, set weight component to w+shrinkageVal. If -shrinkageVal < w < shrinkageVal, set weight component to 0.
Equivalently, set weight component to signum(w) * max(0.0, abs(w) - shrinkageVal)