Efficient Optimization of Dominant Set Clustering with Frank-Wolfe Algorithms
Paper in proceeding, 2022

We study Frank-Wolfe algorithms - standard, pairwise, and away-steps - for efficient optimization of Dominant Set Clustering. We present a unified and computationally efficient framework to employ the different variants of Frank-Wolfe methods, and we investigate its effectiveness via several experimental studies. In addition, we provide explicit convergence rates for the algorithms in terms of the so-called Frank-Wolfe gap. The theoretical analysis has been specialized to Dominant Set Clustering and covers consistently the different variants.

frank-wolfe optimization

replicator dynamics

dominant set

clustering

Author

Carl Johnell

Student at Chalmers

Morteza Haghir Chehreghani

Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI

International Conference on Information and Knowledge Management, Proceedings

915-924
9781450392365 (ISBN)

31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Atlanta, USA,

Subject Categories

Computational Mathematics

Control Engineering

Signal Processing

DOI

10.1145/3511808.3557306

More information

Latest update

10/26/2023