Nearly Sharp Sufficient Conditions on Exact Sparsity Pattern Recovery

Dept. of Stat., Columbia Univ., New York, NY, USA
IEEE Transactions on Information Theory (Impact Factor: 2.33). 08/2011; 57(7):4672 - 4679. DOI: 10.1109/TIT.2011.2145670
Source: IEEE Xplore


Consider the n -dimensional vector y = X β+ε where β ∈ BBR p has only k nonzero entries and ε ∈ BBR n is a Gaussian noise. This can be viewed as a linear system with sparsity constraints corrupted by noise, where the objective is to estimate the sparsity pattern of β given the observation vector y and the measurement matrix X . First, we derive a nonasymptotic upper bound on the probability that a specific wrong sparsity pattern is identified by the maximum-likelihood estimator. We find that this probability depends (inversely) exponentially on the difference of || X β||2 and the l 2 -norm of X β projected onto the range of columns of X indexed by the wrong sparsity pattern. Second, when X is randomly drawn from a Gaussian ensemble, we calculate a nonasymptotic upper bound on the probability of the maximum-likelihood decoder not declaring (partially) the true sparsity pattern. Consequently, we obtain sufficient conditions on the sample size n that guarantee almost surely the recovery of the true sparsity pattern. We find that the required growth rate of sample size n matches the growth rate of previously established necessary conditions.

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Available from: Kamiar Rahnama Rad, Jul 10, 2014
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    • "These necessary conditions are tightened in [17], and a comparison between dense and sparse ensembles is performed. In [18], sufficient conditions are derived and shown to be tight in a scaling-law sense by comparison to the necessary conditions of [17]. For information-theoretic limits with respect to other performance metrics, see [13], [19], [20]. "
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