Publications in Scientific Journals:
F. Meghdouri, F. Iglesias Vazquez, T. Zseby:
"Modeling Data with Observers";
Intelligent Data Analysis,
26
(2021),
3;
785
- 803.
English abstract:
Compact data models have become relevant due to the massive, ever-increasing generation of data. We propose Observers-based Data Modeling (ODM), a lightweight algorithm to extract low density data models (aka coresets) that are suitable for both static and stream data analysis. ODM coresets keep data internal structures while alleviating computational costs of machine learning during evaluation phases accounting for a O(n log n) worst-case complexity. We compare ODM with previous proposals in classification, clustering, and outlier detection. Results show the preponderance of ODM for obtaining the best trade-off in accuracy, versatility, and speed.
Keywords:
big data, low density models, coresets
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.3233/IDA-215741
Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_300951.pdf
Created from the Publication Database of the Vienna University of Technology.