Boosting is one of the two most successful machine learning methods with SVM. It uses gradient descent to an empirical loss function. When the step sizes are small, it is computationally efficient way to approximate Lasso. When a nuclear norm penalization is applied to L2 loss, we have the low-rank regularization arising from the Netflix competition. A subset of the netflix data will be investigated.