Compressed Counting Meets Compressed Sensing

27th Conference on Learning Theory

数学科学;计算机科学

Ping Li [e-mail] ; Department of Statistics and Biostatistics ; Rutgers University ; Piscataway ; NJ 08854 ; USA

PID  :  119653

来源: CEUR

【 摘 要 】

Compressed sensing (sparse signal recovery) has been a popular and important research topic in recent years. By observing that natural signals (e.g., images or network data) are often nonnegative, we propose a framework for nonnegative signal recovery using Compressed Counting (CC). CC is a technique built on maximallyskewed stable random projections originally developed for data stream computations (e.g., entropy estimations). Our recovery procedure is computationally efficient in that it requires only one linear scan of the coordinates. In our settings, the signal xRN is assumed to be nonnegative, i.e., xi0,i. We prove that, when(0, 0.5], it suffices to useM = (C+o(1)) (N