liu.seSearch for publications in DiVA
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Fast manifold learning based on Riemannian normal coordinates
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
Laboratory of Mathematics in Imaging Harvard Medical School, Boston, USA.
Linköpings universitet, Matematiska institutionen, Tillämpad matematik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.ORCID-id: 0000-0002-9091-4724
2005 (engelsk)Inngår i: Image Analysis: 14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19-22, 2005. Proceedings / [ed] Heikki Kalviainen, Jussi Parkkinen, Arto Kaarna., Springer Berlin/Heidelberg, 2005, s. 920-Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We present a novel method for manifold learning, i.e. identification of the low-dimensional manifold-like structure present in a set of data points in a possibly high-dimensional space. The main idea is derived from the concept of Riemannian normal coordinates. This coordinate system is in a way a generalization of Cartesian coordinates in Euclidean space. We translate this idea to a cloud of data points in order to perform dimension reduction. Our implementation currently uses Dijkstra’s algorithm for shortest paths in graphs and some basic concepts from differential geometry. We expect this approach to open up new possibilities for analysis of e.g. shape in medical imaging and signal processing of manifold-valued signals, where the coordinate system is “learned” from experimental high-dimensional data rather than defined analytically using e.g. models based on Lie-groups.

sted, utgiver, år, opplag, sider
Springer Berlin/Heidelberg, 2005. s. 920-
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 3540
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-28773DOI: 10.1007/11499145_93Lokal ID: 13955ISBN: 978-3-540-26320-3 (tryckt)ISBN: 978-3-540-31566-7 (tryckt)OAI: oai:DiVA.org:liu-28773DiVA, id: diva2:249585
Konferanse
14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19-22, 2005
Tilgjengelig fra: 2009-10-09 Laget: 2009-10-09 Sist oppdatert: 2018-02-08bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Personposter BETA

Brun, AndersWestin, Carl-FredrikHerberthson, MagnusKnutsson, Hans

Søk i DiVA

Av forfatter/redaktør
Brun, AndersWestin, Carl-FredrikHerberthson, MagnusKnutsson, Hans
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric

doi
isbn
urn-nbn
Totalt: 885 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf