Distilled sensing: selective sampling for sparse signal recovery

Jarvis Haupt, Rui Castro, Robert Nowak
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:216-223, 2009.

Abstract

A selective sampling methodology called Distilled Sensing (DS) is proposed for recovering sparse signals in noise. DS exploits the fact that it is often easier to rule out locations that do not contain signal than it is to detect the locations of non-zero signal components. We formalize this observation and use it to devise a sequential selective sensing strategy that focuses sensing/measurement resources towards the signal subspace. This adaptivity in sensing results in rather surprising gains in sparse signal recovery compared to non-adaptive sensing. We show that exponentially weaker sparse signals can be recovered via DS compared with conventional non-adaptive sensing.

Cite this Paper


BibTeX
@InProceedings{pmlr-v5-haupt09a, title = {Distilled sensing: selective sampling for sparse signal recovery}, author = {Jarvis Haupt and Rui Castro and Robert Nowak}, pages = {216--223}, year = {2009}, editor = {David van Dyk and Max Welling}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/haupt09a/haupt09a.pdf}, url = {http://proceedings.mlr.press/v5/haupt09a.html}, abstract = {A selective sampling methodology called Distilled Sensing (DS) is proposed for recovering sparse signals in noise. DS exploits the fact that it is often easier to rule out locations that do not contain signal than it is to detect the locations of non-zero signal components. We formalize this observation and use it to devise a sequential selective sensing strategy that focuses sensing/measurement resources towards the signal subspace. This adaptivity in sensing results in rather surprising gains in sparse signal recovery compared to non-adaptive sensing. We show that exponentially weaker sparse signals can be recovered via DS compared with conventional non-adaptive sensing.} }
Endnote
%0 Conference Paper %T Distilled sensing: selective sampling for sparse signal recovery %A Jarvis Haupt %A Rui Castro %A Robert Nowak %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-haupt09a %I PMLR %J Proceedings of Machine Learning Research %P 216--223 %U http://proceedings.mlr.press %V 5 %W PMLR %X A selective sampling methodology called Distilled Sensing (DS) is proposed for recovering sparse signals in noise. DS exploits the fact that it is often easier to rule out locations that do not contain signal than it is to detect the locations of non-zero signal components. We formalize this observation and use it to devise a sequential selective sensing strategy that focuses sensing/measurement resources towards the signal subspace. This adaptivity in sensing results in rather surprising gains in sparse signal recovery compared to non-adaptive sensing. We show that exponentially weaker sparse signals can be recovered via DS compared with conventional non-adaptive sensing.
RIS
TY - CPAPER TI - Distilled sensing: selective sampling for sparse signal recovery AU - Jarvis Haupt AU - Rui Castro AU - Robert Nowak BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics PY - 2009/04/15 DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-haupt09a PB - PMLR SP - 216 DP - PMLR EP - 223 L1 - http://proceedings.mlr.press/v5/haupt09a/haupt09a.pdf UR - http://proceedings.mlr.press/v5/haupt09a.html AB - A selective sampling methodology called Distilled Sensing (DS) is proposed for recovering sparse signals in noise. DS exploits the fact that it is often easier to rule out locations that do not contain signal than it is to detect the locations of non-zero signal components. We formalize this observation and use it to devise a sequential selective sensing strategy that focuses sensing/measurement resources towards the signal subspace. This adaptivity in sensing results in rather surprising gains in sparse signal recovery compared to non-adaptive sensing. We show that exponentially weaker sparse signals can be recovered via DS compared with conventional non-adaptive sensing. ER -
APA
Haupt, J., Castro, R. & Nowak, R.. (2009). Distilled sensing: selective sampling for sparse signal recovery. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in PMLR 5:216-223

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