Supervised discriminant Isomap with maximum margin graph regularization for dimensionality reduction

作者：

Highlights：

• Two novel DR methods are proposed to extract critical discriminative information.

• Neighborhood size parameters of DR models are decided adaptively via data similarity.

• Outside data can be projected directly through SD-IsoP that prevents over-fitting.

• Margins between classes in reduced space can be maximized by proposed methods.

摘要

•Two novel DR methods are proposed to extract critical discriminative information.•Neighborhood size parameters of DR models are decided adaptively via data similarity.•Outside data can be projected directly through SD-IsoP that prevents over-fitting.•Margins between classes in reduced space can be maximized by proposed methods.

论文关键词：Isomap,Supervised dimensionality reduction,Discriminant analysis,Linear embedding,Classification

论文评审过程：Received 13 January 2021, Revised 10 March 2021, Accepted 15 April 2021, Available online 23 April 2021, Version of Record 13 May 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115055