Seeing is Learning in High Dimensions: The Synergy Between Dimensionality Reduction and Machine Learning

Alexandru Telea*, Alister Machado, Yu Wang

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

High-dimensional data are a key study object for both machine learning (ML) and information visualization. On the visualization side, dimensionality reduction (DR) methods, also called projections, are the most suited techniques for visual exploration of large and high-dimensional datasets. On the ML side, high-dimensional data are generated and processed by classifiers and regressors, and these techniques increasingly require visualization for explanation and exploration. In this paper, we explore how both fields can help each other in achieving their respective aims. In more detail, we present both examples that show how DR can be used to understand and engineer better ML models (seeing helps learning) and also applications of DL for improving the computation of direct and inverse projections (learning helps seeing). We also identify existing limitations of DR methods used to assist ML and of ML techniques applied to improve DR. Based on the above, we propose several high-impact directions for future work that exploit the analyzed ML-DR synergy.

Original languageEnglish
Article number279
Number of pages25
JournalSN Computer Science
Volume5
Issue number3
DOIs
Publication statusPublished - Mar 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

Keywords

  • Explainable AI
  • Multidimensional projections
  • Visual quality metrics

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