Evaluation and Selection of Autoencoders for Expressive Dimensionality Reduction of Spatial Ensembles

Hamid Gadirov*, Steffen Frey*, Gleb Tkachev, Thomas Ertl

*Corresponding author for this work

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This paper evaluates how autoencoder variants with different architectures and parameter settings affect the quality of 2D projections for spatial ensembles, and proposes a guided selection approach based on partially labeled data. Extracting features with autoencoders prior to applying techniques like UMAP substantially enhances the projection results and better conveys spatial structures and spatio-temporal behavior. Our comprehensive study demonstrates substantial impact of different variants, and shows that it is highly data-dependent which ones yield the best possible projection results. We propose to guide the selection of an autoencoder configuration for a specific ensemble based on projection metrics. These metrics are based on labels, which are however prohibitively time-consuming to obtain for the full ensemble. Addressing this, we demonstrate that a small subset of labeled members suffices for choosing an autoencoder configuration. We discuss results featuring various types of autoencoders applied to two fundamentally different ensembles featuring thousands of members: channel structures in soil from Markov chain Monte Carlo and time-dependent experimental data on droplet-film interaction.
Original languageEnglish
Title of host publicationAdvances in Visual Computing
Subtitle of host publicationISVC 2021
Editors G. Bebis , Vassilis Athitsos, Tong Yan, Manfred d Lau
Place of PublicationCham
Number of pages13
ISBN (Electronic)978-3-030-90439-5
ISBN (Print)978-3-030-90438-8
Publication statusPublished - 2021
Event16th International Symposium on Visual Computing: ISVC 2021 -
Duration: 4-Oct-20216-Oct-2021

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Symposium on Visual Computing


  • Feature learning
  • Machine learning
  • Dimensionality reduction
  • Clustering
  • Ensemble visualization

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