Change search
CiteExportLink to record
Permanent link

Direct link
Cite
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
KERNEL REGRESSION FOR GRAPH SIGNAL PREDICTION IN PRESENCE OF SPARSE NOISE
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
Ecole Polytech Fed Lausanne, Signal Proc Lab LTS4, Lausanne, Switzerland..
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.ORCID iD: 0000-0003-2638-6047
2019 (English)In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2019, p. 5426-5430Conference paper, Published paper (Refereed)
Abstract [en]

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse noise is handled using appropriate use of l(1)-norm along-with use of l(2)-norm in a convex cost function. For optimization of the cost function, we propose an iteratively reweighted least-squares (IRLS) approach that is suitable for kernel substitution or kernel trick due to availability of a closed form solution. Simulations using real-world temperature data show efficacy of our proposed method, mainly for limited-size training datasets.

Place, publisher, year, edition, pages
IEEE , 2019. p. 5426-5430
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords [en]
Kernel regression, graph signal processing, Sparse noise, graph-Laplacian, iteratively reweighted least squares
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-261058DOI: 10.1109/ICASSP.2019.8682979ISI: 000482554005132Scopus ID: 2-s2.0-85068972923ISBN: 978-1-4799-8131-1 (print)OAI: oai:DiVA.org:kth-261058DiVA, id: diva2:1356650
Conference
44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), MAY 12-17, 2019, Brighton, ENGLAND
Note

QC 20191002

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Venkitaraman, ArunChatterjee, Saikat

Search in DiVA

By author/editor
Venkitaraman, ArunChatterjee, Saikat
By organisation
Information Science and Engineering
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 2 hits
CiteExportLink to record
Permanent link

Direct link
Cite
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