A parallel ADMM-based convex clustering method
|Lidija Fodor , Dušan Jakovetić, Danijela Boberić Krstićev and Srđan Škrbić
|A parallel ADMM-based convex clustering method
|Convex clustering has received recently an increased interest as a valuable method for unsupervised learning. Unlike conventional clustering methods such as k-means, its formulation corresponds to solving a convex optimization problem and hence, alleviates initialization and local minima problems. However, while several algorithms have been proposed to solve convex clustering formulations, including those based on the alternating direction method of multipliers (ADMM), there is currently a limited body of work on developing scalable parallel and distributed algorithms and solvers for convex clustering. In this paper, we develop a parallel, ADMM-based method, for a modified convex clustering sum-of-norms (SON) formulation for master–worker architectures, where the data to be clustered are partitioned across a number of worker nodes, and we provide its efficient, open-source implementation (available on Parallel ADMM-based convex clustering. https://github.com/lidijaf/Parallel-ADMM-based-convex-clustering. Accessed on 10 June 2022) for high-performance computing (HPC) cluster environments. Extensive numerical evaluations on real and synthetic data sets demonstrate a high degree of scalability and efficiency of the method, when compared with existing alternative solvers for convex clustering.
|Title of the journal:
|EURASIP Journal on Advances in Signal Processing
|Year of Publication:
|Is this a peer-reviewed publication?
|Is this a joint public/private publication?
Project Coordinator: Sofoklis Efremidis
Institution: Maggioli SPA
Duration: 36 months
Participating organisations: 14
Number of countries: 10
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 952690. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.