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We study Frank-Wolfe algorithms - standard, pairwise, and away-steps - forefficient optimization of Dominant Set Clustering. We present a unified andcomputationally efficient framework to employ the different variants ofFrank-Wolfe methods, and we investigate its effectiveness via severalexperimental studies. In addition, we provide explicit convergence rates forthe algorithms in terms of the so-called Frank-Wolfe gap. The theoreticalanalysis has been specialized to Dominant Set Clustering and coversconsistently the different variants.

Efficient Optimization of Dominant Set Clustering with Frank-Wolfe Algorithms