Distilled sensing: Selective sampling for sparse signal recovery

Jarvis Haupt, Rui Castro, Robert Nowak

Research output: Contribution to journalConference articlepeer-review

19 Scopus citations

Abstract

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 subspace. This adaptivity in sensing results in rather surprising gains in sparse signal recovery-dramatically weaker sparse signals can be recovered using DS compared with conventional non-adaptive sensing procedures.

Original languageEnglish (US)
Pages (from-to)216-223
Number of pages8
JournalJournal of Machine Learning Research
Volume5
StatePublished - Dec 1 2009
Event12th International Conference on Artificial Intelligence and Statistics, AISTATS 2009 - Clearwater, FL, United States
Duration: Apr 16 2009Apr 18 2009

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