论文标题
通过Frank-Wolfe算法有效地优化主导集聚类
Efficient Optimization of Dominant Set Clustering with Frank-Wolfe Algorithms
论文作者
论文摘要
我们研究Frank -Wolfe算法 - 标准，成对和离散的算法 - 有效地优化了主要的集合聚类。我们提出了一个统一和计算上有效的框架，以采用弗兰克 - 沃尔夫方法的不同变体，并通过几项实验研究研究了其有效性。此外，我们根据所谓的弗兰克·沃尔夫（Frank-Wolfe）差距为算法提供明确的收敛率。理论分析已专门用于占主导地位集群，并始终覆盖不同的变体。
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.
