Record link: 
http://hdl.handle.net/20.500.12708/208524
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Title: 
Scalable Interactive Data Visualization
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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
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Publisher DOI: 
10.1007/978-3-031-70371-3_34
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Chen, Florian 
Gärtner, Thomas 
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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) 
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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 
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ISBN: 
978-3-031-70371-3
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Volume: 
14948
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DOI of the book: 
10.1007/978-3-031-70371-3
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Date (published): 
22-Aug-2024
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Event name: 
Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2024) 
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Event date: 
9-Sep-2024 - 13-Sep-2024
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Event place: 
Vilnius, Lithuania
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Number of Pages: 
5
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Publisher: 
Springer, Cham
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Peer reviewed: 
Yes
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Keywords: 
Interactive visualization; iterative optimization; kernel PCA; projected gradient descent; variational autoencoder
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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.
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Project title: 
Structured Data Learning with Generalized Similarities: ICT22-059 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds) 
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Link (external): 
https://drive.google.com/file/d/1k2bd6ig7Cp5IiRnvFp7YLGrX9m98TMzC/view?usp=sharing
https://github.com/faguodev/invis
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Research Areas: 
Mathematical and Algorithmic Foundations: 50%
Computer Science Foundations: 50%
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Science Branch: 
1020 - Informatik: 100%
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Appears in Collections:
Conference Paper

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