We use L 1 regularization because we can obtain a sparse parameter vector, for which many of the parameter values are exactly zero.
learning with L 1 regularization naturally has an intrinsic effect of feature selection, which results in an efficient and interpretable inference with almost the same performance as L 2 regularization (Gao et al., 2007).