Machine Learning Methods in Visualisation for Big Data
Permanent URI for this community
Browse
Browsing Machine Learning Methods in Visualisation for Big Data by Author "Hauser, Helwig"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item On KDE-based Brushing in Scatterplots and how it Compares to CNN-based Brushing(The Eurographics Association, 2019) Fan, Chaoran; Hauser, Helwig; Archambault, Daniel and Nabney, Ian and Peltonen, JaakkoIn this paper, we investigate to which degree the human should be involved into the model design and how good the empirical model can be with more careful design. To find out, we extended our previously published Mahalanobis brush (the best current empirical model in terms of accuracy for brushing points in a scatterplot) by further incorporating the data distribution information that is captured by the kernel density estimation (KDE). Based on this work, we then include a short discussion between the empirical model, designed in detail by an expert and the deep learning-based model that is learned from user data directly.Item Revealing Multimodality in Ensemble Weather Prediction(The Eurographics Association, 2021) Galmiche, Natacha; Hauser, Helwig; Spengler, Thomas; Spensberger, Clemens; Brun, Morten; Blaser, Nello; Archambault, Daniel and Nabney, Ian and Peltonen, JaakkoEnsemble methods are widely used to simulate complex non-linear systems and to estimate forecast uncertainty. However, visualizing and analyzing ensemble data is challenging, in particular when multimodality arises, i.e., distinct likely outcomes. We propose a graph-based approach that explores multimodality in univariate ensemble data from weather prediction. Our solution utilizes clustering and a novel concept of life span associated with each cluster. We applied our method to historical predictions of extreme weather events and illustrate that our method aids the understanding of the respective ensemble forecasts.