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Theory Seminar
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation
Chris SchwiegelshohnAarhus university
WHERE:
4941 Beyster BuildingMap
WHEN:
Wednesday, November 15, 2023 @ 3:30 pm - 4:30 pm
This event is free and open to the publicAdd to Google Calendar
This event is free and open to the publicAdd to Google Calendar
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Moreso than other areas, algorithms used for large scale data analysis in high dimensional spaces rely on randomization. From the celebrated Johnson-Lindenstrauss lemma, to streaming and sketching algorithms, to even popular heuristics like k-means++, randomization seems to have a strong competitive edge. A natural question to ask whether this is necessary?
In this talk we make present results that mark the first step in this direction. For k-clustering problems such as k-means and k-median, we provide deterministic algorithms for dimension reduction, coresets, and polynomial time approximation schemes. All algorithms are fixed parameter tractable in the number of clusters k and the desired precision parameter epsilon. Based on joint work with Vincent Cohen-Addad and David Saulpic.