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Distilled Sensing: Active sensing for sparse recovery

Distilled Sensing: Active sensing for sparse recovery

This video was recorded at Workshop on Sparsity in Machine Learning and Statistics, Cumberland Lodge 2009. The study and use of sparse representations in data-rich applications has garnered signicant attention in the signal processing, statistics, and machine learning communities. In the present work we describe a novel sensing procedure called Distilled Sensing (DS), which is a sequential and adaptive approach for recovering sparse signals in noise. Passive sensing approaches, currently the most widespread data collection methods, involve non- adaptive data collection procedures that are completely specied before any data is observed. In contrast, DS collects data in a sequential and adaptive manner. Often such procedures are known as active sensing or sequential experimental design, and... Show More


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