TY - GEN
T1 - Online Group-Sparse Regression Using the Covariance Fitting Criterion
AU - Kronvall, Ted
AU - Adalbjörnsson, Stefan Ingi
AU - Nadig, Santhosh
AU - Jakobsson, Andreas
N1 - I
PY - 2017
Y1 - 2017
N2 - In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved byr eformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regularization level, for which a time-recursive implementation is derived. Using a proximal gradient step for lowering the computational cost, the proposed method may effectively cope with data sequences consisting of both stationary and non-stationary signals, such as transients, and/or amplitude modulated signals. Numerical examples illustrates the efficacy of the proposed method for both coherent Gaussian dictionaries and for the multi-pitch estimation problem.
AB - In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved byr eformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regularization level, for which a time-recursive implementation is derived. Using a proximal gradient step for lowering the computational cost, the proposed method may effectively cope with data sequences consisting of both stationary and non-stationary signals, such as transients, and/or amplitude modulated signals. Numerical examples illustrates the efficacy of the proposed method for both coherent Gaussian dictionaries and for the multi-pitch estimation problem.
M3 - Paper in conference proceeding
VL - CFP1740S-USB
T3 - European Signal Processing Conference (EUSIPCO)
BT - Proceedings of the 25th European Signal Processing Conference (EUSIPCO)
PB - EURASIP
ER -