Bayesian compressive sensing and projection optimization

Shihao Ji; Lawrence Carin

doi:10.1145/1273496.1273544

Bayesian compressive sensing and projection optimization

Shihao Ji, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

38 Scopus citations

Abstract

This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured accurately based on K

Original language	English (US)
Title of host publication	ACM International Conference Proceeding Series
Pages	377-384
Number of pages	8
DOIs	https://doi.org/10.1145/1273496.1273544
State	Published - Aug 23 2007
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2021-02-09

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10.1145/1273496.1273544

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title = "Bayesian compressive sensing and projection optimization",

abstract = "This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed {"}compressive sensing{"}, in which a real signal of dimension N is measured accurately based on K",

author = "Shihao Ji and Lawrence Carin",

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year = "2007",

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doi = "10.1145/1273496.1273544",

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AU - Carin, Lawrence

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PY - 2007/8/23

Y1 - 2007/8/23

N2 - This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured accurately based on K

AB - This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal of dimension N is measured accurately based on K

UR - http://portal.acm.org/citation.cfm?doid=1273496.1273544

UR - http://www.scopus.com/inward/record.url?scp=34547990838&partnerID=8YFLogxK

U2 - 10.1145/1273496.1273544

DO - 10.1145/1273496.1273544

M3 - Conference contribution

SP - 377

EP - 384

BT - ACM International Conference Proceeding Series

ER -

Bayesian compressive sensing and projection optimization

Abstract

Bibliographical note

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