Given a redundant dictionary of basis vectors (or atoms), our goal is to \ufb01nd maximally sparse representations of signals. Previously, we have argued that a sparse Bayesian learning (SBL) framework is particularly well-suited for this task, showing that it has far fewer local minima than other Bayesian-inspired strategies. In this paper, we provide further evidence for this claim by proving a restricted equivalence condition, based on the distribution of the nonzero generating model weights, whereby the SBL solution will equal the maximally sparse representation. We also prove that if these nonzero weights are drawn from an approximate Jeffreys prior, then with probability approaching one, our equivalence condition is satis\ufb01ed. Finally, we motivate the worst-case scenario for SBL and demonstrate that it is still better than the most widely used sparse representation algorithms. These include Basis Pursuit (BP), which is based on a convex relaxation of the \u21130 (quasi)-norm, and Orthogonal Matching Pursuit (OMP), a simple greedy strategy that iteratively selects basis vectors most aligned with the current residual.<\/p>\n","protected":false},"excerpt":{"rendered":"
Given a redundant dictionary of basis vectors (or atoms), our goal is to \ufb01nd maximally sparse representations of signals. Previously, we have argued that a sparse Bayesian learning (SBL) framework is particularly well-suited for this task, showing that it has far fewer local minima than other Bayesian-inspired strategies. 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