Sparse Sampling of Signal Innovations

Sparse Sampling of Signal Innovations: Theory, Algorithms and Performance Bounds

Sparse Signal Sampling using Noisy Linear Projections

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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...

Inference from Sparse Sampling

In some statistical learning applications, the individual objects that are encountered are complex, behaveprobabilistically, and are encountered too briefly for the learner to form more than a fragmentary and unreliablerecord of the properties of each individual. In some cases, only previously collected sporadic survey data isavailable to the learner. In other cases, studying an...

Sparse sampling heuristic search

In the article A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes, Kearns et al show that there’re now theoratical boundary’s to solve an mdp with an infinite state space in a such a way that the running time has no dependency’s on the size of the statespace. To come to this conclusion they introduce an algorithm that uses a sparse look-a-head tree to selec...