Efficient Optimization of Dominant Set Clustering with Frank-Wolfe Algorithms

TL;DR

This paper develops a unified, efficient framework for optimizing Dominant Set Clustering using various Frank-Wolfe algorithms, providing theoretical convergence analysis and experimental validation.

Contribution

It introduces a unified framework for Frank-Wolfe variants in Dominant Set Clustering, with explicit convergence rates and comprehensive experimental evaluation.

Findings

The framework is computationally efficient.

Explicit convergence rates are established.

Experimental results demonstrate effectiveness.

Abstract

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.