Nonconvex clustering via ℓ0 fusion penalized regression

作者：

Highlights：

• A novel penalized clustering model is proposed based on the ℓ0 fusion penalty.

• We derive some properties and optimality conditions of the proposed model.

• Efficient optimization algorithm is designed to solve the model.

• Experimental results show that our method is superior to the state-of-the-art methods.

摘要

•A novel penalized clustering model is proposed based on the ℓ0 fusion penalty.•We derive some properties and optimality conditions of the proposed model.•Efficient optimization algorithm is designed to solve the model.•Experimental results show that our method is superior to the state-of-the-art methods.

论文关键词：Penalized clustering,ℓ0 fusion penalty,Nonconvex discontinuous optimization,Alternating direction method of multipliers

论文评审过程：Received 20 February 2021, Revised 12 March 2022, Accepted 3 April 2022, Available online 5 April 2022, Version of Record 9 April 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108689