Very Fast Streaming Submodular Function Maximization

Author: S. Buschjäger, P. Honysz, L. Pfahler, K. Morik
Journal: ECML PKDD 2021: Machine Learning and Knowledge Discovery in Databases. Research Track
Year: 2021

Citation information

S. Buschjäger, P. Honysz, L. Pfahler, K. Morik,
ECML PKDD 2021: Machine Learning and Knowledge Discovery in Databases. Research Track,
2021,
151–166,
Springer, Cham,
https://doi.org/10.1007/978-3-030-86523-8_10

Data summarization has become a valuable tool in understanding even terabytes of data. Due to their compelling theoretical properties, submodular functions have been in the focus of summarization algorithms. These algorithms offer worst-case approximations guarantees to the expense of higher computation and memory requirements. However, many practical applications do not fall under this worst-case, but are usually much more well-behaved. In this paper, we propose a new submodular function maximization algorithm called ThreeSieves, which ignores the worst-case, but delivers a good solution in high probability. It selects the most informative items from a data-stream on the fly and maintains a provable performance on a fixed memory budget. In an extensive evaluation, we compare our method against 6 other methods on 8 different datasets with and without concept drift. We show that our algorithm outperforms current state-of-the-art algorithms and, at the same time, uses fewer resources. Last, we highlight a real-world use-case of our algorithm for data summarization in gamma-ray astronomy.