TY - GEN
T1 - Parallelizable algorithms for the selection of grouped variables
AU - Mateos, Gonzalo
AU - Bazerque, Juan Andrés
AU - Giannakis, Georgios B.
PY - 2011
Y1 - 2011
N2 - Well-appreciated in statistics for its ability to select relevant grouped features (factors) in linear regression models, the group-Lasso estimator has been fruitfully applied to diverse signal processing problems including RF spectrum cartography and robust layered sensing. These applications motivate the distributed group-Lasso algorithm developed in this paper, that can be run by a network of wireless sensors, or, by multiple processors to balance the load of a single computational unit. After reformulating the group-Lasso cost into a separable form, it is iteratively minimized using the method of multipliers to obtain parallel per agent and per factor estimate updates given by vector soft-thresholding operations. Through affordable inter-agent communication of sparse messages, the local estimates provably consent to the global group-Lasso solution. Specializing to a single agent network, or, to univariate factors, efficient (distributed) Lasso solvers are rediscovered as a byproduct.
AB - Well-appreciated in statistics for its ability to select relevant grouped features (factors) in linear regression models, the group-Lasso estimator has been fruitfully applied to diverse signal processing problems including RF spectrum cartography and robust layered sensing. These applications motivate the distributed group-Lasso algorithm developed in this paper, that can be run by a network of wireless sensors, or, by multiple processors to balance the load of a single computational unit. After reformulating the group-Lasso cost into a separable form, it is iteratively minimized using the method of multipliers to obtain parallel per agent and per factor estimate updates given by vector soft-thresholding operations. Through affordable inter-agent communication of sparse messages, the local estimates provably consent to the global group-Lasso solution. Specializing to a single agent network, or, to univariate factors, efficient (distributed) Lasso solvers are rediscovered as a byproduct.
KW - (group-) Lasso
KW - Sparsity
KW - distributed estimation
KW - linear regression
KW - parallel optimization
UR - http://www.scopus.com/inward/record.url?scp=79954564617&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79954564617&partnerID=8YFLogxK
U2 - 10.1109/DSP-SPE.2011.5739228
DO - 10.1109/DSP-SPE.2011.5739228
M3 - Conference contribution
AN - SCOPUS:79954564617
SN - 9781612842271
T3 - 2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings
SP - 295
EP - 300
BT - 2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings
T2 - 2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011
Y2 - 4 January 2011 through 7 January 2011
ER -