  Bagging, a powerful ensemble method from machine learning, improves the
performance of unstable predictors. Although the power of Bagging has been
shown mostly in classification problems, we demonstrate the success of
employing Bagging in sparse regression over the baseline method (L1
minimization). The framework employs the generalized version of the original
Bagging with various bootstrap ratios. The performance limits associated with
different choices of bootstrap sampling ratio L/m and number of estimates K is
analyzed theoretically. Simulation shows that the proposed method yields
state-of-the-art recovery performance, outperforming L1 minimization and
Bolasso in the challenging case of low levels of measurements. A lower L/m
ratio (60% - 90%) leads to better performance, especially with a small number
of measurements. With the reduced sampling rate, SNR improves over the original
Bagging by up to 24%. With a properly chosen sampling ratio, a reasonably small
number of estimates K = 30 gives satisfying result, even though increasing K is
discovered to always improve or at least maintain the performance.
