Consistent Sampling and Signal Recovery

Quasi-Random Sequences for Signal Sampling and Recovery

The problem of reconstruction of band-limited signals from sampled and noisy observations is studied. It is proposed to sample a signal at quasi-random points, that form a deterministic sequence with properties resembling a random variable being uniformly distributed. Such quasi-random points can be easily and efficiently generated yielding signal reconstruction algorithms with the improved acc...

Distilled sensing: selective sampling for sparse signal recovery

A selective sampling procedure called distilled sensing (DS) is proposed, and shown to be an effective method for recovering sparse signals in noise. Based on the notion that it is often easier to rule out locations that do not contain signal than it is to directly identify non-zero signal components, DS is a sequential method that systematically focuses sensing resources towards the signal sub...

Consistent Weighted Sampling

We describe an efficient procedure for sampling representatives from a weighted set such that for any weightings S and T , the probability that the two choose the same sample is equal to the Jaccard similarity between them: Pr[sample(S) = sample(T )] = ∑ x min(S(x), T (x)) ∑ x max(S(x), T (x)) where sample(S) is a pair (x, y) with 0 < y ≤ S(x). The sampling process takes expected computation li...

Consistent Subset Sampling

Consistent sampling is a technique for specifying, in small space, a subset S of a potentially large universe U such that the elements in S satisfy a suitably chosen sampling condition. Given a subset I ⊆ U it should be possible to quickly compute I ∩ S, i.e., the elements in I satisfying the sampling condition. Consistent sampling has important applications in similarity estimation, and estima...

Database Prototyping through Consistent Sampling