Very Fast Streaming Submodular Function Maximization

• Published in: Machine Learning and Knowledge Discovery in Databases. Research Track Vol. 12977; pp. 151 - 166
• Main Authors: Buschjäger, Sebastian, Honysz, Philipp-Jan, Pfahler, Lukas, Morik, Katharina
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Data summarization; Streaming data; Submodular function maximization
• ISBN: 3030865223; 9783030865221
• ISSN: 0302-9743; 1611-3349
• DOI: 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 the focus of summarization algorithms. Submodular function maximization is a well-studied problem with a variety of algorithms available. These algorithms usually offer worst-case guarantees to the expense of higher computation and memory requirements. However, many practical applications do not fall under this mathematical worst-case but are usually much more well-behaved. We propose a new submodular function maximization algorithm called ThreeSieves that ignores the worst-case and thus uses fewer resources. Our algorithm selects the most informative items from a data-stream on the fly and maintains a provable performance in most cases on a fixed memory budget. In an extensive evaluation, we compare our method against 6 state-of-the-art algorithms on 8 different datasets including data with and without concept drift. We show that our algorithm outperforms the current state-of-the-art in the majority of cases and, at the same time, uses fewer resources.