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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...
A cooperative approach to the sensing task of wireless cognitive radio (CR) networks is introduced based on a spatial model of the power spectrum density (PSD). The model entails a basis expansion of the PSD in frequency, weighted by unknown spatial functions that are estimated from CR measurements. A novel model estimator is developed using a least-squares (LS) criterion regularized with a smoothing...
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