Word embedding dimensionality reduction using dynamic variance thresholding (DyVaT)

作者：

Highlights：

• Variance thresholding to reduce dimensionality requires manual threshold selection.

• Dynamic variance thresholding (DyVaT) uses the kneedle algorithm to set a threshold.

• DyVat can be used recursively to create large ontologies using a single root word.

摘要

•Variance thresholding to reduce dimensionality requires manual threshold selection.•Dynamic variance thresholding (DyVaT) uses the kneedle algorithm to set a threshold.•DyVat can be used recursively to create large ontologies using a single root word.

论文关键词：Dimensionality reduction,Machine learning,Natural language processing,Learning styles,Feature selection

论文评审过程：Received 20 October 2021, Revised 13 June 2022, Accepted 11 July 2022, Available online 16 July 2022, Version of Record 22 July 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118157