@conference{10902/21904,
year = {2020},
url = {http://hdl.handle.net/10902/21904},
abstract = {In this paper, we describe an efficient iterative algorithm for finding sparse solutions to a linear system. Apart from the well-known L1 norm regularization, we introduce an additional cost term promoting solutions without too-close activations. This additional term, which is expressed as a sum of cross-products of absolute values, makes the problem nonconvex and difficult to solve. However, the application of the successive convex approximations approach allows us to obtain an efficient algorithm consisting in the solution of a sequence of iteratively reweighted LASSO problems. Numerical simulations on randomly generated waveforms and ECG signals show the good performance of the proposed method.},
organization = {This work has been partly funded by the Spanish government through the KERMES excellence network (ref. TEC2016-81900-REDT).},
publisher = {Institute of Electrical and Electronics Engineers, Inc.},
publisher = {28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands, 2020, 2045-2049},
title = {Efficient Iteratively reweighted LASSO algorithm for cross-products penalized sparse solutions},
author = {Luengo García, David and Vía Rodríguez, Javier and Trigano, Thomas},
}