Ensemble Visualization With Variational Autoencoder
This addresses the need for better visualization of data ensembles, such as in weather forecasting, but appears incremental as it applies an existing VAE method to a specific domain.
The paper tackled the problem of visualizing data ensembles by constructing structured probabilistic representations in latent spaces using a variational autoencoder, resulting in latent spaces that follow multivariate standard Gaussian distributions for analytical computation of confidence intervals and density estimation, with preliminary results on a weather forecasting ensemble demonstrating effectiveness and versatility.
We present a new method to visualize data ensembles by constructing structured probabilistic representations in latent spaces, i.e., lower-dimensional representations of spatial data features. Our approach transforms the spatial features of an ensemble into a latent space through feature space conversion and unsupervised learning using a variational autoencoder (VAE). The resulting latent spaces follow multivariate standard Gaussian distributions, enabling analytical computation of confidence intervals and density estimation of the probabilistic distribution that generates the data ensemble. Preliminary results on a weather forecasting ensemble demonstrate the effectiveness and versatility of our method.