Visualizing high-dimensional industrial process based on deep reinforced discriminant features and a stacked supervised t-distributed stochastic neighbor embedding network

作者：

Highlights：

• A stacked reinforced discriminant autoencoder is proposed for feature extraction.

• The proposed stacked autoencoder and MRMR are combined for feature selection.

• A stacked supervised t-SNE is proposed for data visualization.

• A new visualization-based process monitoring method is introduced.

摘要

•A stacked reinforced discriminant autoencoder is proposed for feature extraction.•The proposed stacked autoencoder and MRMR are combined for feature selection.•A stacked supervised t-SNE is proposed for data visualization.•A new visualization-based process monitoring method is introduced.

论文关键词：Visual process monitoring,Stacked auto-encoder,Feature extraction,Visualization,T-stochastic neighbor embedding

论文评审过程：Received 4 September 2020, Revised 20 November 2020, Accepted 8 June 2021, Available online 4 August 2021, Version of Record 13 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115389