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Extreme learning machine for graph signal processing
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0002-2718-0262
2018 (English)In: 2018 26th European Signal Processing Conference (EUSIPCO), European Signal Processing Conference, EUSIPCO , 2018, p. 136-140, article id 8553088Conference paper, Published paper (Refereed)
Abstract [en]

In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is smooth over a given graph. Simulation results with real data confirm that such regularization helps significantly when the available training data is limited in size and corrupted by noise.

Place, publisher, year, edition, pages
European Signal Processing Conference, EUSIPCO , 2018. p. 136-140, article id 8553088
Series
European Signal Processing Conference, ISSN 2219-5491
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-241525DOI: 10.23919/EUSIPCO.2018.8553088ISI: 000455614900028Scopus ID: 2-s2.0-85059801757ISBN: 9789082797015 (print)OAI: oai:DiVA.org:kth-241525DiVA, id: diva2:1281853
Conference
26th European Signal Processing Conference, EUSIPCO 2018, Rome, Italy, 3 September 2018 through 7 September 2018
Note

QC 20180123

Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-02-01Bibliographically approved

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Chatterjee, SaikatHändel, Peter

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf