2022-06-28-armacki22a.md

title	booktitle	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Gradient Based Clustering	Proceedings of the 39th International Conference on Machine Learning	We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality with respect to cluster assignments and cluster center positions. The approach is an iterative two step procedure (alternating between cluster assignment and cluster center updates) and is applicable to a wide range of functions, satisfying some mild assumptions. The main advantage of the proposed approach is a simple and computationally cheap update rule. Unlike previous methods that specialize to a specific formulation of the clustering problem, our approach is applicable to a wide range of costs, including non-Bregman clustering methods based on the Huber loss. We analyze the convergence of the proposed algorithm, and show that it converges to the set of appropriately defined fixed points, under arbitrary center initialization. In the special case of Bregman cost functions, the algorithm converges to the set of centroidal Voronoi partitions, which is consistent with prior works. Numerical experiments on real data demonstrate the effectiveness of the proposed method.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	armacki22a	0	Gradient Based Clustering	929	947	929-947	929	false	Armacki, Aleksandar and Bajovic, Dragana and Jakovetic, Dusan and Kar, Soummya	given family Aleksandar Armacki given family Dragana Bajovic given family Dusan Jakovetic given family Soummya Kar	2022-06-28		Proceedings of the 39th International Conference on Machine Learning	162	inproceedings	date-parts 2022 6 28	https://proceedings.mlr.press/v162/armacki22a/armacki22a.pdf