Adaptive sampling for fast sparsity pattern recovery
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/18421
Tipus de documentText en actes de congrés
Data publicació2011
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
In this paper we propose a low complexity adaptive algorithm
for lossless compressive sampling and reconstruction of
sparse signals. Consider a sparse non-negative real signal x
containing only k << n non-zero values. The sampling process
obtains m measurements by a linear projection y = Ax
and, in order to minimize the complexity, we quantize them
to binary values. We also define the measurement matrix A
to be binary and sparse, enabling the use of a simple message
passing algorithm over a graph. We show how to adaptively
construct this matrix in a multi-stage process that sequentially
reduces the search space until the sparsity pattern is
perfectly recovered. As verified by simulation results, the
process requires O(n) operations and O(k log(n/k)) samples
CitacióRamirez, F.; Matas, D.; Lamarca, M. Adaptive sampling for fast sparsity pattern recovery. A: European Signal Processing Conference. "EUSIPCO 2011: 19th European Signal Processing Conference". Barcelona: 2011, p. 348-352.
ISBN2076-1465 (ISSN)
Fitxers | Descripció | Mida | Format | Visualitza |
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1569424357.pdf | ADAPTIVE SAMPLING FOR FAST SPARSITY PATTERN RECOVERY | 337,6Kb | Visualitza/Obre |