Publikation:

Inverting Multidimensional Scaling Projections Using Data Point Multilateration

Lade...
Vorschaubild

Dateien

Blumberg_2-q0024d5hnxgm5.pdf
Blumberg_2-q0024d5hnxgm5.pdfGröße: 5.62 MBDownloads: 6

Datum

2024

Autor:innen

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

ArXiv-ID

Internationale Patentnummer

Link zur Lizenz
oops

Angaben zur Forschungsförderung

Institutionen der Bundesrepublik Deutschland: 03EI1048D
Deutsche Forschungsgemeinschaft (DFG): 251654672

Projekt

Open Access-Veröffentlichung
Open Access Bookpart
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

EL-ASSADY, Mennatallah, Hrsg., Hans-Jörg SCHULZ, Hrsg.. EuroVis Workshop on Visual Analytics (EuroVA 2024). Eindhoven: Eurographics, 2024. ISBN 978-3-03868-253-0. Verfügbar unter: doi: 10.2312/eurova.20241112

Zusammenfassung

Current inverse projection methods are often complex, hard to predict, and may require extensive parametrization. We present a new technique to compute inverse projections of Multidimensional Scaling (MDS) projections with minimal parametrization. We use mutilateration, a method used for geopositioning, to find data values for unknown 2D points, i.e., locations where no data point is projected. Being based on a geometrical relationship, our technique is more interpretable than comparable machine learning-based approaches and can invert 2-dimensional projections up to |D|−1 dimensional spaces given a minimum of |D| data points. We qualitatively and quantitatively compare our technique with existing inverse projection techniques on synthetic and real-world datasets using mean-squared errors (MSEs) and gradient maps. When MDS captures data distances well, our technique shows performance similar to existing approaches. While our method may show higher MSEs when inverting projected data samples, it produces smoother gradient maps, indicating higher predictability when inverting unseen points.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

EuroVis Workshop on Visual Analytics (EuroVA 2024), 27. Mai 2024, Odense, Denmark
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690BLUMBERG, Daniela, Yu WANG, Alexandru TELEA, Daniel A. KEIM, Frederik L. DENNIG, 2024. Inverting Multidimensional Scaling Projections Using Data Point Multilateration. EuroVis Workshop on Visual Analytics (EuroVA 2024). Odense, Denmark, 27. Mai 2024. In: EL-ASSADY, Mennatallah, Hrsg., Hans-Jörg SCHULZ, Hrsg.. EuroVis Workshop on Visual Analytics (EuroVA 2024). Eindhoven: Eurographics, 2024. ISBN 978-3-03868-253-0. Verfügbar unter: doi: 10.2312/eurova.20241112
BibTex
@inproceedings{Blumberg2024Inver-70143,
  year={2024},
  doi={10.2312/eurova.20241112},
  title={Inverting Multidimensional Scaling Projections Using Data Point Multilateration},
  isbn={978-3-03868-253-0},
  publisher={Eurographics},
  address={Eindhoven},
  booktitle={EuroVis Workshop on Visual Analytics (EuroVA 2024)},
  editor={El-Assady, Mennatallah and Schulz, Hans-Jörg},
  author={Blumberg, Daniela and Wang, Yu and Telea, Alexandru and Keim, Daniel A. and Dennig, Frederik L.}
}
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/123456789/70143">
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Dennig, Frederik L.</dc:creator>
    <dc:creator>Blumberg, Daniela</dc:creator>
    <dc:contributor>Wang, Yu</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/70143"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-06-18T06:51:45Z</dc:date>
    <dcterms:title>Inverting Multidimensional Scaling Projections Using Data Point Multilateration</dcterms:title>
    <dcterms:issued>2024</dcterms:issued>
    <dc:language>eng</dc:language>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/70143/1/Blumberg_2-q0024d5hnxgm5.pdf"/>
    <dc:contributor>Blumberg, Daniela</dc:contributor>
    <dc:contributor>Telea, Alexandru</dc:contributor>
    <dc:contributor>Dennig, Frederik L.</dc:contributor>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/70143/1/Blumberg_2-q0024d5hnxgm5.pdf"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-06-18T06:51:45Z</dcterms:available>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Wang, Yu</dc:creator>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dcterms:abstract>Current inverse projection methods are often complex, hard to predict, and may require extensive parametrization. We present a new technique to compute inverse projections of Multidimensional Scaling (MDS) projections with minimal parametrization. We use mutilateration, a method used for geopositioning, to find data values for unknown 2D points, i.e., locations where no data point is projected. Being based on a geometrical relationship, our technique is more interpretable than comparable machine learning-based approaches and can invert 2-dimensional projections up to |D|−1 dimensional spaces given a minimum of |D| data points. We qualitatively and quantitatively compare our technique with existing inverse projection techniques on synthetic and real-world datasets using mean-squared errors (MSEs) and gradient maps. When MDS captures data distances well, our technique shows performance similar to existing approaches. While our method may show higher MSEs when inverting projected data samples, it produces smoother gradient maps, indicating higher predictability when inverting unseen points.</dcterms:abstract>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dc:creator>Telea, Alexandru</dc:creator>
  </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
Diese Publikation teilen