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On the Performance of Sparse Recovery via L_p-minimization (0<=p <=1)

Clinical Orthopaedics and Related Research, (2010)

Cited: 55|Views29
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Abstract

It is known that a high-dimensional sparse vector x* in R^n can be recovered from low-dimensional measurements y= A^{m*n} x* (m<n) . In this paper, we investigate the recovering ability of l_p-minimization (0<=p<=1) as p varies, where l_p-minimization returns a vector with the least l_p ``norm'' among all the vectors x satisfying Ax=y. Be...More

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