View in EDS HTML Full Text PDF Full Text

How to visualize high‐dimensional data.

Saved in:
Bibliographic Details
Title: How to visualize high‐dimensional data.
Authors: Mrowka, Ralf (AUTHOR), Schmauder, Ralf (AUTHOR)
Source: Acta Physiologica. Oct2024, Vol. 240 Issue 10, p1-4. 4p.
Subjects: Scientific literature, Principal components analysis, Regional development, Data structures, Blood pressure measurement
Abstract: This article discusses the visualization of high-dimensional data in the field of physiology. The authors emphasize the importance of clarifying the axes and variables represented in diagrams to ensure accurate interpretation. They explain that traditional methods like principal component analysis (PCA) may not be sufficient for high-dimensional data and introduce nonlinear techniques like t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). These methods allow for the visualization of complex data and have been widely used in various fields, including neurophysiology, immunology, cancer research, and infectious diseases. The authors caution that the interpretation of these plots requires careful consideration due to the nonlinear transformations involved. They also mention ongoing efforts to improve these methods. Overall, the article highlights the need for clear explanations of high-dimensional plots in presentations and acknowledges the interdisciplinary nature of physiology and the rapid development of methods in the field. [Extracted from the article]
Copyright of Acta Physiologica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 179808225
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: How to visualize high‐dimensional data.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Mrowka%2C+Ralf%22">Mrowka, Ralf</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Schmauder%2C+Ralf%22">Schmauder, Ralf</searchLink> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Acta+Physiologica%22">Acta Physiologica</searchLink>. Oct2024, Vol. 240 Issue 10, p1-4. 4p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Scientific+literature%22">Scientific literature</searchLink><br /><searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Regional+development%22">Regional development</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Blood+pressure+measurement%22">Blood pressure measurement</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This article discusses the visualization of high-dimensional data in the field of physiology. The authors emphasize the importance of clarifying the axes and variables represented in diagrams to ensure accurate interpretation. They explain that traditional methods like principal component analysis (PCA) may not be sufficient for high-dimensional data and introduce nonlinear techniques like t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). These methods allow for the visualization of complex data and have been widely used in various fields, including neurophysiology, immunology, cancer research, and infectious diseases. The authors caution that the interpretation of these plots requires careful consideration due to the nonlinear transformations involved. They also mention ongoing efforts to improve these methods. Overall, the article highlights the need for clear explanations of high-dimensional plots in presentations and acknowledges the interdisciplinary nature of physiology and the rapid development of methods in the field. [Extracted from the article]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Acta Physiologica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=179808225
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/apha.14219
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 4
        StartPage: 1
    Subjects:
      – SubjectFull: Scientific literature
        Type: general
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Regional development
        Type: general
      – SubjectFull: Data structures
        Type: general
      – SubjectFull: Blood pressure measurement
        Type: general
    Titles:
      – TitleFull: How to visualize high‐dimensional data.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Mrowka, Ralf
      – PersonEntity:
          Name:
            NameFull: Schmauder, Ralf
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Text: Oct2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 17481708
          Numbering:
            – Type: volume
              Value: 240
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Acta Physiologica
              Type: main
ResultId 1