TY - GEN
T1 - Active set strategy for high-dimensional non-convex sparse optimization problems
AU - Boisbunon, Aurelie
AU - Flamary, Remi
AU - Rakotomamonjy, Alain
PY - 2014/1/1
Y1 - 2014/1/1
N2 - The use of non-convex sparse regularization has attracted much interest when estimating a very sparse model on high dimensional data. In this work we express the optimality conditions of the optimization problem for a large class of non-convex regularizers. From those conditions, we derive an efficient active set strategy that avoids the computing of unnecessary gradients. Numerical experiments on both generated and real life datasets show a clear gain in computational cost w.r.t. the state of the art when using our method to obtain very sparse solutions.
AB - The use of non-convex sparse regularization has attracted much interest when estimating a very sparse model on high dimensional data. In this work we express the optimality conditions of the optimization problem for a large class of non-convex regularizers. From those conditions, we derive an efficient active set strategy that avoids the computing of unnecessary gradients. Numerical experiments on both generated and real life datasets show a clear gain in computational cost w.r.t. the state of the art when using our method to obtain very sparse solutions.
KW - Non-convex optimization
KW - sparsity
KW - very large scale
UR - https://www.scopus.com/pages/publications/84905215863
U2 - 10.1109/ICASSP.2014.6853851
DO - 10.1109/ICASSP.2014.6853851
M3 - Conference contribution
AN - SCOPUS:84905215863
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1517
EP - 1521
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -