Distances, Neighborhoods, or Dimensions? : Projection Literacy for the Analysis of Multivariate Data

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Proceedings of VisxAI : Workshop on Visualization for AI Explainability. 2018
Zusammenfassung

Projections are some of the most common methods for presenting high-dimensional datasets on a 2D display. While these techniques provide overviews that highlight relations between observations, they are unavoidably subject to change depending on chosen configurations. Hence, the same projection technique can depict multiple compositions of the same dataset, depending on its parameter setting. Furthermore, projection techniques differ in their underlying assumptions and computation mechanisms, favoring the preservation of either distances, neighborhoods, or dimensions. This article aims to shed light on the similarities and differences of a multitude of projection techniques, the influence of features and parameters on data-representations, and give a data-driven intuition on the relation of projections. We postulate that, depending on the task and data, a different choice of projection technique, or a combination of such, might lead to a more effective view.

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VisxAI : Workshop on Visualization for AI Explainability, 22. Okt. 2018, Berlin
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ISO 690STREEB, Dirk, Rebecca KEHLBECK, Dominik JÄCKLE, Mennatallah EL-ASSADY, 2018. Distances, Neighborhoods, or Dimensions? : Projection Literacy for the Analysis of Multivariate Data. VisxAI : Workshop on Visualization for AI Explainability. Berlin, 22. Okt. 2018. In: Proceedings of VisxAI : Workshop on Visualization for AI Explainability. 2018
BibTex
@inproceedings{Streeb2018Dista-45031,
  year={2018},
  title={Distances, Neighborhoods, or Dimensions? : Projection Literacy for the Analysis of Multivariate Data},
  url={https://visxprojections.dbvis.de/client/index.html},
  booktitle={Proceedings of VisxAI : Workshop on Visualization for AI Explainability},
  author={Streeb, Dirk and Kehlbeck, Rebecca and Jäckle, Dominik and El-Assady, Mennatallah}
}
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