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COMBINED MODELING OF SPARSE AND DENSE NOISE IMPROVES BAYESIAN RVM
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES).ORCID iD: 0000-0001-6992-5771
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES).ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES).ORCID iD: 0000-0002-6855-5868
2014 (English)In: 2014 PROCEEDINGS OF THE 22ND EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE , 2014, p. 1841-1845Conference paper, Published paper (Refereed)
Abstract [en]

Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or data loss. To handle sparse noise, existing methods simultaneously estimate sparse noise and sparse signal of interest. For estimating the sparse signal, without estimating the sparse noise, we construct a Relevance Vector Machine (RVM). In the RVM, sparse noise and ever present dense noise are treated through a combined noise model. Through simulations, we show the efficiency of new RVM for three applications: kernel regression, housing price prediction and compressed sensing.

Place, publisher, year, edition, pages
IEEE , 2014. p. 1841-1845
Series
European Signal Processing Conference, ISSN 2076-1465
Keywords [en]
Robust regression, Bayesian learning, Relevance vector machine, Compressed sensing
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-243801ISI: 000393420200370ISBN: 978-0-9928626-1-9 (print)OAI: oai:DiVA.org:kth-243801DiVA, id: diva2:1286372
Conference
22nd European Signal Processing Conference (EUSIPCO), SEP 01-05, 2014, Lisbon, PORTUGAL
Note

QC 20190206

Available from: 2019-02-06 Created: 2019-02-06 Last updated: 2019-08-21Bibliographically approved

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Sundin, MartinChatterjee, SaikatJansson, Magnus

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  • apa
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Output format
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