A greedy algorithm for sparse precision matrix approximation
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Precision matrix estimation is an important problem in statistical data analysis. This paper introduces a fast sparse precision matrix estimation algorithm, namely GISS^ρ, which is originally introduced for compressive sensing. The algorithm GISS^ρ is derived based on l_1 minimization while with the computation advantage of greedy algorithms. We analyze the asymptotic convergence rate of the proposed GISS^ρ for sparse precision matrix estimation and sparsity recovery properties with respect to the stopping criteria. Finally, we numerically compare GISS^ρ to other sparse recovery algorithms, such as ADMM and HTP in three settings of precision matrix estimation. The numerical results show the advantages of the proposed algorithm.
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