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2010

Distributed sparse linear regression

9 years 1 months ago
Distributed sparse linear regression
The Lasso is a popular technique for joint estimation and continuous variable selection, especially well-suited for sparse and possibly under-determined linear regression problems. This paper develops algorithms to estimate the regression coefficients via Lasso when the training data are distributed across different agents, and their communication to a central processing unit is prohibited for e.g., communication cost or privacy reasons. A motivating application is explored in the context of wireless communications, whereby sensing cognitive radios collaborate to estimate the radio-frequency power spectrum density. Attaining different tradeoffs between complexity and convergence speed, three novel algorithms are obtained after reformulating the Lasso into a separable form, which is iteratively minimized using the alternating-direction method of multipliers so as to gain the desired degree of parallelization. Interestingly, the per agent estimate updates are given by simple soft-thresho...
Gonzalo Mateos, Juan Andrés Bazerque, Georg
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TSP
Authors Gonzalo Mateos, Juan Andrés Bazerque, Georgios B. Giannakis
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