Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81056
Title: An efficient semismooth Newton based algorithm for convex clustering
Authors: Yuan, Y
Sun, D 
Toh, KC
Issue Date: 2018
Source: Proceedings of Machine Learning Research, 2018, v. 80, p. 5718-5726 How to cite?
Journal: Proceedings of Machine Learning Research 
Abstract: Clustering is a fundamental problem in unsupervised learning. Popular methods like K-means, may suffer from instability as they are prone to get stuck in its local minima. Recently, the sum-of-norms (SON) model (also known as clustering path), which is a convex relaxation of hierarchical clustering model, has been proposed in (Lindsten et al., 2011) and (Hocking et al., 2011). Although numerical algorithms like alternating direction method of multipliers (ADMM) and alternating minimization algorithm (AMA) have been proposed to solve convex clustering model (Chi & Lange, 2015), it is known to be very challenging to solve large-scale problems. In this paper, we propose a semismooth Newton based augmented Lagrangian method for large-scale convex clustering problems. Extensive numerical experiments on both simulated and real data demonstrate that our algorithm is highly efficient and robust for solving large-scale problems. Moreover, the numerical results also show the superior performance and scalability of our algorithm comparing to existing first-order methods.
Description: 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, 10-15 July 2018
URI: http://hdl.handle.net/10397/81056
ISSN: 2640-3498
Rights: Copyright 2018 by the author(s).
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 Inter-national License (http://creativecommons.org/licenses/by/4.0/legalcode) (human readable summary at http://creativecommons.org/licenses/by/4.0)
The following publication Yuan, Y., Sun, D., & Toh, K. C. (2018). An efficient semismooth Newton based algorithm for convex clustering. Proceedings of Machine Learning Research, 2018, v. 80, p. 5718-5726 is available at http://proceedings.mlr.press/v80/yuan18a.html
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