Title: Sparse Recovery via Bootstrapping:  Collaborative or Independent?. Abstract: Sparse regression problems have traditionally been solved using all available measurements simultaneously. However, this approach fails in challenging scenarios such as when the noise level is high or there are missing data / adversarial samples. 
We propose JOBS (Joint-Sparse Optimization via Bootstrap Samples) -- a \emph{collaborative} sparse-regression framework on bootstrapped samples from the pool of available measurements via a joint-sparse constraint to ensure support consistency. In comparison to traditional bagging which solves sub-problems in an \emph{independent} fashion across bootstrapped samples, JOBS achieves state-of-the-art performance with the added advantage of having a sparser solution while requiring a lower number of observation samples.

Analysis of theoretical performance limits is employed to determine critical optimal parameters: the number of bootstrap samples $K$ and the number of elements $L$ in each bootstrap sample. Theoretical results indicate a better bound than Bagging (i.e. higher probability of achieving the same or better performance). Simulation results are used to validate this parameter selection. JOBS is robust to adversarial samples that fool the baseline method, as shown by better generalization in an image reconstruction task where the adversary has similar occlusions or alignment as the test sample. Furthermore, JOBS also improves discriminative performance in a facial recognition task in a sparse-representation-based classification setting.