Sparse Random Features Algorithm as Coordinate Descent in Hilbert Space Shou-De Lin

In our experiments, the Sparse Random Feature algorithm obtains a sparse solution that requires less memory and prediction time, while maintaining comparable performance on regression and classification tasks.