New Accumulative Score Function Based Bound For Sparsity Level of L1 Minimization

• DOI: 10.48550/arxiv.1410.2447
• Publication Date: 2014-01-01
• Authors: Sheng Han; Suzhen Wang; Zhiguo Zhang
• Keywords: Minification; Matrix (chemical analysis)

This paper discusses a fundamental problem in compressed sensing: the sparse recoverability of L1 minimization with an arbitrary sensing matrix. We develop new accumulative score function (ASF) to provide lower bound for recoverable sparsity level (SL) matrix while preserving low computational complexity. first define each row matrix, and then ASF sums up large scores until total reaches 0.5. Interestingly, number involved rows summation is reliable SL. It further proved that provides sharper SL than coherence also investigate underlying relationship between classical RIC achieve RIC-based