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Combined Modelling of Sparse and Dense noise improves Bayesian RVM
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Communication Theory.ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6855-5868
2014 (English)In: Proceedings of the 22nd European Signal Processing Conference (EUSIPCO), 2014, IEEE conference proceedings, 2014, 1841-1845 p.Conference paper, Oral presentation with published abstract (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 conference proceedings, 2014. 1841-1845 p.
Keyword [en]
Robust regression, Bayesian learning, Relevance vector machine, Compressed sensing
National Category
Signal Processing
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-165603OAI: oai:DiVA.org:kth-165603DiVA: diva2:808656
Conference
The 22nd European Signal Processing Conference (EUSIPCO), 1-5 Sep. 2014,Lisbon
Note

QC 20150512

Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2015-10-20Bibliographically approved

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fulltext(713 kB)76 downloads
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Chatterjee, SaikatJansson, Magnus

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Citation style
  • apa
  • harvard1
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Output format
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