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Compressed Counting Meets Compressed Sensing

Compressed Counting Meets Compressed Sensing

This video was recorded at 27th Annual Conference on Learning Theory (COLT), Barcelona 2014. 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 maximally-skewed α-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 x∈RN is assumed to be nonnegative, i.e., xi≥0,∀ i. We prove that, when α∈(0, 0.5], it suffices to use M=(Cα+o(1))ϵ−α(∑Ni=1xαi)logN/δ measurements so that, with probability 1−δ, all coordinates will be recovered within ϵ additive precision, in one scan of the coordinates. The constant Cα=1 when α→0 and Cα=π/2 when α=0.5. In particular, when α→0, the required number of measurements is essentially M=KlogN/δ, where K=∑Ni=11{xi≠0} is the number of nonzero coordinates of the signal.

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