Meghdouri, F., Iglesias Vazquez, F., & Zseby, T. (2022). Modeling Data with Observers. Intelligent Data Analysis, 26(3), 785–803. https://doi.org/10.3233/ida-215741
Artificial Intelligence; Theoretical Computer Science; big data; Computer Vision and Pattern Recognition; low density models; coresets
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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.
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Research Areas:
Telecommunication: 50% Information Systems Engineering: 25% Logic and Computation: 25%
