Semi-automatic data annotation guided by feature space projection

作者：

Highlights：

• Feature space projections to increase semi-supervised learning.

• Data annotation using feature space projections outperforms automatic methods.

• Combining automatic and user-driven labeling methods improves annotation and classification results.

• Confidence measures reduce human labeling effort as compared to fully-manual labeling.

摘要

•Feature space projections to increase semi-supervised learning.•Data annotation using feature space projections outperforms automatic methods.•Combining automatic and user-driven labeling methods improves annotation and classification results.•Confidence measures reduce human labeling effort as compared to fully-manual labeling.

论文关键词：Semi-supervised learning,Unsupervised feature learning,Interactive data annotation,Autoencoder-neural networks,Data visualization

论文评审过程：Received 2 July 2019, Revised 5 June 2020, Accepted 19 August 2020, Available online 22 August 2020, Version of Record 24 August 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107612