Efficient Optimization of Dominant Set Clustering with Frank-Wolfe Algorithms
This work addresses computational efficiency in clustering for data analysis, but it is incremental as it adapts existing optimization methods to a specific clustering technique.
The paper tackles the problem of optimizing Dominant Set Clustering by applying Frank-Wolfe algorithms, resulting in a unified framework with explicit convergence rates and experimental validation of its effectiveness.
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.