Coherent Visualization of 2D Scalar Field Contour Ensembles With Probabilistic Latent Space Modeling
Cenyang Wu (Peking University Health Science Center), Runhao Lin (National Institute of Health Data Science, Peking University), Qinhan Yu (Peking University), Liang Zhou (Peking University Health Science Center)
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Presentation
- Session
- I'm not so certain
- Time
- Thursday, Nov 12, 09:00 – 09:12 (US/Eastern) · session 08:00 – 09:30
- Room
- Hall Essex north
- Presenting from
- Boston
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Abstract
We present a new visualization method for contour ensembles through probabilistic modeling. We aim to improve the coherence between different visual representations, such as contour boxplots and density plots for a 2D scalar field ensemble. We model each ensemble member with a probabilistic representation in the latent space, i.e., a lower-dimensional representation of spatial data features, of a variational autoencoder (VAE). Thereafter, efficient data depth computation and uncertainty-aware clustering are supported based on a matrix of pair-wise similarity measurements of members. We estimate the underlying probability distribution by leveraging the power of VAE to create density plots that align more coherently with member distributions than existing methods. The effectiveness of our method is evaluated through numerical comparisons with existing techniques, and visualization examples of synthetic and real-world ensemble datasets.
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