Recursive ℓ_1,∞ Group lasso

01/29/2011
by   Yilun Chen, et al.
0

We introduce a recursive adaptive group lasso algorithm for real-time penalized least squares prediction that produces a time sequence of optimal sparse predictor coefficient vectors. At each time index the proposed algorithm computes an exact update of the optimal ℓ_1,∞-penalized recursive least squares (RLS) predictor. Each update minimizes a convex but nondifferentiable function optimization problem. We develop an online homotopy method to reduce the computational complexity. Numerical simulations demonstrate that the proposed algorithm outperforms the ℓ_1 regularized RLS algorithm for a group sparse system identification problem and has lower implementation complexity than direct group lasso solvers.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset