具有非一致选择随机支撑的子矩阵及其稀疏逼近
Submatrices with non-uniformly selected random supports and insights into sparse approximation
摘要：
本文给出了具有非均匀分布支撑的随机子矩阵范数的尾界。我们将这些结果应用于稀疏近似，并对阈值，正交匹配追踪和基追踪的平均情况下的性能进行分析。作为这些结果的一个应用程序，我们的感知字典，以提高在非均匀的情况下的平均性能，并测试其性能数值。
英文摘要：
In this paper we derive tail bounds on the norms of random submatrices with non-uniformly distributed supports. We apply these results to sparse approximation and conduct an analysis of the average case performance of thresholding, Orthogonal Matching Pursuit and Basis Pursuit. As an application of these results we characterise sensing dictionaries to improve average performance in the non-uniform case and test their performance numerically.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
