General representational automata using deep neural networks

作者：

Highlights：

• A novel unsupervised minimum attribute instance selection (UMAIS) labeling method.

• A correlation based attribute powerset generation (APSG) method.

• A representational automata (pictoral model) to show the relationship of attributes.

摘要

•A novel unsupervised minimum attribute instance selection (UMAIS) labeling method.•A correlation based attribute powerset generation (APSG) method.•A representational automata (pictoral model) to show the relationship of attributes.

论文关键词：Powerset,Representational learning,Automata,Unsupervised,Unlabeled,Categorical,Transition,Renewable energy,Bankruptcy

论文评审过程：Received 11 December 2018, Revised 6 June 2019, Accepted 10 June 2019, Available online 13 June 2019, Version of Record 25 July 2019.

论文官网地址：https://doi.org/10.1016/j.datak.2019.06.004