One-step spectral rotation clustering for imbalanced high-dimensional data

作者：

Highlights：

• Select a set of balanced samples across classes to reduce outliers and noisy samples.

• Conduct dimensionality reduction by both subspace learning and feature selection.

• Consider sample selection and feature selection in a unified learning framework.

摘要

•Select a set of balanced samples across classes to reduce outliers and noisy samples.•Conduct dimensionality reduction by both subspace learning and feature selection.•Consider sample selection and feature selection in a unified learning framework.

论文关键词：Spectral rotation clustering,Dimensionality reduction,Imbalanced data

论文评审过程：Received 12 July 2020, Revised 26 August 2020, Accepted 11 September 2020, Available online 29 September 2020, Version of Record 29 September 2020.

论文官网地址：https://doi.org/10.1016/j.ipm.2020.102388