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Recursive \ell _{1,\infty } Group Lasso

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2 Author(s)
Yilun Chen ; Dept. of Electr. Eng. & Comput. Sci., Univ. of Michigan, Ann Arbor, MI, USA ; Hero, A.O.

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 ℓ1,∞-penalized recursive least squares (RLS) predictor. Each update minimizes a convex but nondifferentiable function optimization problem. We develop an on-line homotopy method to reduce the computational complexity. Numerical simulations demonstrate that the proposed algorithm outperforms the ℓ1 regularized RLS algorithm for a group sparse system identification problem and has lower implementation complexity than direct group lasso solvers.

Published in:

Signal Processing, IEEE Transactions on  (Volume:60 ,  Issue: 8 )

Date of Publication:

Aug. 2012

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