Ensemble selection with joint spectral clustering and structural sparsity

作者：

Highlights：

• First unsupervised ensemble selection method with joint clustering and sparsity.

• A prediction space leveraging the prediction power of an ensemble is the basis of the method.

• The data in the prediction space, which globally describe the prediction distribution of the ensemble, are unlabeled.

• Method’s ensemble results are robust to test instances and uses less space.

摘要

•First unsupervised ensemble selection method with joint clustering and sparsity.•A prediction space leveraging the prediction power of an ensemble is the basis of the method.•The data in the prediction space, which globally describe the prediction distribution of the ensemble, are unlabeled.•Method’s ensemble results are robust to test instances and uses less space.

论文关键词：Ensemble selection,Structural sparsity,Unsupervised selection,Spectral clustering,Dynamic and static,Robustness

论文评审过程：Received 19 October 2020, Revised 16 April 2021, Accepted 19 May 2021, Available online 29 May 2021, Version of Record 17 June 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2021.108061