Sparse binary matrices are of great interest in the field of sparse recovery, nonnegative compressed sensing, statistics in networks, and theoretical computer science. This class of matrices makes it possible to perform signal recovery with lower storage costs and faster decoding algorithms. In particular, Bernoulli () matrices formed by independent identically distributed (i.i.d.) Bernoulli () random variables are of practical relevance in the context of noise-blind recovery in nonnegative compressed sensing.

中文翻译：

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通过扩展器使用稀疏伯努利矩阵进行鲁棒稀疏恢复


稀疏二元矩阵在稀疏恢复、非负压缩感知、网络统计和理论计算机科学领域引起了极大的兴趣。此类矩阵使得能够以更低的存储成本和更快的解码算法执行信号恢复。特别是，由独立同分布（iid）伯努利（）随机变量形成的伯努利（）矩阵在非负压缩感知中的噪声盲恢复背景下具有实际意义。