Scalable Interactive Data Visualization

Chen, Florian; Gärtner, Thomas

doi:10.1007/978-3-031-70371-3_34

Record link:

http://hdl.handle.net/20.500.12708/208524

Title:

Citation:

Chen, F., & Gärtner, T. (2024). Scalable Interactive Data Visualization. In A. Bifet, P. Daniusis, & J. Davis (Eds.), Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VIII (pp. 429–433). Springer. https://doi.org/10.1007/978-3-031-70371-3_34

Publisher DOI:

10.1007/978-3-031-70371-3_34

Publication Type:

Inproceedings - Full-Paper Contribution

Language:

English

Authors:

Chen, Florian
Gärtner, Thomas

Organisational Unit:

E194-06 - Forschungsbereich Machine Learning
E056-10 - Fachbereich SecInt-Secure and Intelligent Human-Centric Digital Technologies
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML)

Published in:

Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VIII

ISBN:

978-3-031-70371-3

Volume:

14948

DOI of the book:

10.1007/978-3-031-70371-3

Date (published):

22-Aug-2024

Event name:

Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2024)

Event date:

9-Sep-2024 - 13-Sep-2024

Event place:

Vilnius, Lithuania

Number of Pages:

Publisher:

Springer, Cham

Peer reviewed:

Yes

Keywords:

Interactive visualization; iterative optimization; kernel PCA; projected gradient descent; variational autoencoder

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.

Project title:

Structured Data Learning with Generalized Similarities: ICT22-059 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds)

Link (external):

https://drive.google.com/file/d/1k2bd6ig7Cp5IiRnvFp7YLGrX9m98TMzC/view?usp=sharing
https://github.com/faguodev/invis

Research Areas:

Mathematical and Algorithmic Foundations: 50%
Computer Science Foundations: 50%

Science Branch:

1020 - Informatik: 100%

Appears in Collections:

Conference Paper

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