Modeling data with observers

Article type: Research Article

Authors: Meghdouri, Fares^* | Iglesias Vázquez, Félix | Zseby, Tanja

Affiliations: Telecommunication Institute, TU Wien, Vienna, Austria

Correspondence: [*] Corresponding author: Fares Meghdouri, Telecommunication Institute, TU Wien, Gusshausstrasse 25-25a, 1040, Vienna, Austria. E-mail: fares.meghdouri@tuwien.ac.at.

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

DOI: 10.3233/IDA-215741

Journal: Intelligent Data Analysis, vol. 26, no. 3, pp. 785-803, 2022