org.apache.flink.ml.optimization
L1Regularization
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package optimization
Adds regularization to the loss value
Adds regularization to the loss value
The updated loss is oldLoss + lambda * ||w||_1 where
w is the weight vector and lambda is the regularization parameter
The loss to be updated
The weights used to update the loss
The regularization parameter to be applied
Updated loss
Calculates the new weights based on the gradient and L1 regularization penalty
Calculates the new weights based on the gradient and L1 regularization penalty
Uses the proximal gradient method with L1 regularization to update weights.
The updated weight w - learningRate * gradient is shrunk towards zero
by applying the proximal operator signum(w) * max(0.0, abs(w) - shrinkageVal)
where w is the weight vector, lambda is the regularization parameter,
and shrinkageVal is lambda*learningRate.
The weights to be updated
The gradient according to which we will update the weights
The regularization parameter to be applied
The effective step size for this iteration
Updated weights
L_1regularization penalty.The regularization function is the
L1norm||w||_1withwbeing the weight vector. TheL_1penalty can be used to drive a number of the solution coefficients to 0, thereby producing sparse solutions.