In terms of 1 -norm regularization, feature sparsity can be explained by the following observation: Since every non-zero parameter weight incurs a regularizer penalty of γ|λ i |, its contribution to minimizing the negative loglikelihood has to outweigh this penalty.
parameters values where the gradient at λ = 0 is can be kept zero without changing the optimality of the solution.