Data Sampling in Multi-view and Multi-class Scatterplots via Set Cover Optimization

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2020
Autor:innen
Hu, Ruizhen
Sha, Tingkai
Van Kaick, Oliver
Huang, Hui
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
EU-Projektnummer
DFG-Projektnummer
Angaben zur Forschungsförderung (Freitext)
Projekt
Open Access-Veröffentlichung
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
IEEE Transactions on Visualization and Computer Graphics. Institute of Electrical and Electronics Engineers (IEEE). 2020, 26(1), pp. 739-748. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2019.2934799
Zusammenfassung

We present a method for data sampling in scatterplots by jointly optimizing point selection for different views or classes. Our method uses space-filling curves (Z-order curves) that partition a point set into subsets that, when covered each by one sample, provide a sampling or coreset with good approximation guarantees in relation to the original point set. For scatterplot matrices with multiple views, different views provide different space-filling curves, leading to different partitions of the given point set. For multi-class scatterplots, the focus on either per-class distribution or global distribution provides two different partitions of the given point set that need to be considered in the selection of the coreset. For both cases, we convert the coreset selection problem into an Exact Cover Problem (ECP), and demonstrate with quantitative and qualitative evaluations that an approximate solution that solves the ECP efficiently is able to provide high-quality samplings.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690HU, Ruizhen, Tingkai SHA, Oliver VAN KAICK, Oliver DEUSSEN, Hui HUANG, 2020. Data Sampling in Multi-view and Multi-class Scatterplots via Set Cover Optimization. In: IEEE Transactions on Visualization and Computer Graphics. Institute of Electrical and Electronics Engineers (IEEE). 2020, 26(1), pp. 739-748. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2019.2934799
BibTex
@article{Hu2020-01Sampl-46821,
  year={2020},
  doi={10.1109/TVCG.2019.2934799},
  title={Data Sampling in Multi-view and Multi-class Scatterplots via Set Cover Optimization},
  number={1},
  volume={26},
  issn={1077-2626},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  pages={739--748},
  author={Hu, Ruizhen and Sha, Tingkai and Van Kaick, Oliver and Deussen, Oliver and Huang, Hui}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/46821">
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:title>Data Sampling in Multi-view and Multi-class Scatterplots via Set Cover Optimization</dcterms:title>
    <dc:creator>Huang, Hui</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-09-11T10:48:40Z</dcterms:available>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-09-11T10:48:40Z</dc:date>
    <dc:contributor>Van Kaick, Oliver</dc:contributor>
    <dc:creator>Hu, Ruizhen</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/46821"/>
    <dc:language>eng</dc:language>
    <dc:contributor>Sha, Tingkai</dc:contributor>
    <dc:contributor>Hu, Ruizhen</dc:contributor>
    <dc:creator>Deussen, Oliver</dc:creator>
    <dc:contributor>Huang, Hui</dc:contributor>
    <dc:creator>Sha, Tingkai</dc:creator>
    <dc:creator>Van Kaick, Oliver</dc:creator>
    <dc:contributor>Deussen, Oliver</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:issued>2020-01</dcterms:issued>
    <dcterms:abstract xml:lang="eng">We present a method for data sampling in scatterplots by jointly optimizing point selection for different views or classes. Our method uses space-filling curves (Z-order curves) that partition a point set into subsets that, when covered each by one sample, provide a sampling or coreset with good approximation guarantees in relation to the original point set. For scatterplot matrices with multiple views, different views provide different space-filling curves, leading to different partitions of the given point set. For multi-class scatterplots, the focus on either per-class distribution or global distribution provides two different partitions of the given point set that need to be considered in the selection of the coreset. For both cases, we convert the coreset selection problem into an Exact Cover Problem (ECP), and demonstrate with quantitative and qualitative evaluations that an approximate solution that solves the ECP efficiently is able to provide high-quality samplings.</dcterms:abstract>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Ja
Diese Publikation teilen