Bi-level weighted multi-view clustering via hybrid particle swarm optimization

作者：

Highlights：

• We propose a bi-level fuzzy weighting to discriminate view and feature simultaneously.

• We develop a hybrid optimization based on particle swarm optimization.

• We propose a representative based method to determine hyper-parameters.

• Our method is compared with six clustering algorithm on three real-world datasets.

摘要

•We propose a bi-level fuzzy weighting to discriminate view and feature simultaneously.•We develop a hybrid optimization based on particle swarm optimization.•We propose a representative based method to determine hyper-parameters.•Our method is compared with six clustering algorithm on three real-world datasets.

论文关键词：Multi-view clustering,Feature weighting,k-means,Particle swarm optimization

论文评审过程：Received 8 July 2014, Revised 22 July 2015, Accepted 24 November 2015, Available online 23 December 2015, Version of Record 9 March 2016.

论文官网地址：https://doi.org/10.1016/j.ipm.2015.11.003