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Object org.apache.spark.mllib.optimization.Updater org.apache.spark.mllib.optimization.L1Updater
public class L1Updater
:: DeveloperApi :: 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)
Constructor Summary | |
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L1Updater()
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Method Summary | |
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scala.Tuple2<Vector,Object> |
compute(Vector weightsOld,
Vector gradient,
double stepSize,
int iter,
double regParam)
Compute an updated value for weights given the gradient, stepSize, iteration number and regularization parameter. |
Methods inherited from class Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public L1Updater()
Method Detail |
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public scala.Tuple2<Vector,Object> compute(Vector weightsOld, Vector gradient, double stepSize, int iter, double regParam)
Updater
compute
in class Updater
weightsOld
- - Column matrix of size dx1 where d is the number of features.gradient
- - Column matrix of size dx1 where d is the number of features.stepSize
- - step size across iterationsiter
- - Iteration numberregParam
- - Regularization parameter
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