Scalable Interactive Data Visualization

Florian Chen, Thomas Gärtner

Abstract: We demonstrate scalable interactive visualizations that allow smooth changes to the viewing perspective even on large datasets. The user can interact with the data by selecting and re-locating control points which impacts the location of other data points depending on their similarity. We achieve adaptive frame rates and responsive interaction even for large datasets by using iterative optimization algorithms for knowledge-based kernel PCA and a novel VAE-based embedding.