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
The path kernel: A novel kernel for sequential data
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-1114-6040
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2965-2953
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
2015 (English)In: Pattern Recognition: Applications and Methods : International Conference, ICPRAM 2013 Barcelona, Spain, February 15–18, 2013 Revised Selected Papers / [ed] Ana Fred, Maria De Marsico, Springer Berlin/Heidelberg, 2015, 71-84 p.Conference paper, Published paper (Refereed)
Abstract [en]

We define a novel kernel function for finite sequences of arbitrary length which we call the path kernel. We evaluate this kernel in a classification scenario using synthetic data sequences and show that our kernel can outperform state of the art sequential similarity measures. Furthermore, we find that, in our experiments, a clustering of data based on the path kernel results in much improved interpretability of such clusters compared to alternative approaches such as dynamic time warping or the global alignment kernel.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2015. 71-84 p.
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 318
Keyword [en]
Kernels, Sequences, Pattern recognition, Dynamic time warping, Global alignment, Interpretability, Kernel function, Similarity measure, State of the art, Classification (of information)
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-167388DOI: 10.1007/978-3-319-12610-4_5ISI: 000364822300005Scopus ID: 2-s2.0-84914163004ISBN: 9783319126098 (print)OAI: oai:DiVA.org:kth-167388DiVA: diva2:815584
Conference
2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013; Barcelona; Spain; 15 February 2013 through 18
Note

QC 20150601

Available from: 2015-06-01 Created: 2015-05-22 Last updated: 2015-12-17Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Pokorny, Florian T.Kragic, Danica

Search in DiVA

By author/editor
Baisero, AndreaPokorny, Florian T.Kragic, DanicaEk, Carl Henrik
By organisation
Computer Vision and Active Perception, CVAP
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 292 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