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
Multivariate Discretization for Bayesian Network Structure Learning in Robot Grasping
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
2011 (English)In: IEEE International Conference on Robotics and Automation (ICRA), 2011, IEEE conference proceedings, 2011, 1944-1950 p.Conference paper, Published paper (Refereed)
Abstract [en]

A major challenge in modeling with BNs is learning the structure from both discrete and multivariate continuous data. A common approach in such situations is to discretize continuous data before structure learning. However efficient methods to discretize high-dimensional variables are largely lacking. This paper presents a novel method specifically aiming at discretization of high-dimensional, high-correlated data. The method consists of two integrated steps: non-linear dimensionality reduction using sparse Gaussian process latent variable models, and discretization by application of a mixture model. The model is fully probabilistic and capable to facilitate structure learning from discretized data, while at the same time retain the continuous representation. We evaluate the effectiveness of the method in the domain of robot grasping. Compared with traditional discretization schemes, our model excels both in task classification and prediction of hand grasp configurations. Further, being a fully probabilistic model it handles uncertainty in the data and can easily be integrated into other frameworks in a principled manner.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2011. 1944-1950 p.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-55353DOI: 10.1109/ICRA.2011.5979666ISI: 000324383401026Scopus ID: 2-s2.0-84861203032ISBN: 978-1-61284-386-5 (print)OAI: oai:DiVA.org:kth-55353DiVA: diva2:471497
Conference
IEEE International Conference on Robotics and Automation (ICRA), 2011 , Shanghai
Note

QC 20120110

Available from: 2012-01-02 Created: 2012-01-02 Last updated: 2014-09-30Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Authority records BETA

Kragic Jensfelt, Danica

Search in DiVA

By author/editor
Song, DanEk, Carl HenrikKragic Jensfelt, Danica
By organisation
Computer Vision and Active Perception, CVAPCentre for Autonomous Systems, CAS
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 47 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