Semi-supervised learning for hierarchically structured networks

作者：

Highlights：

• We develop a semi-supervised learning framework of hierarchically structured networks.

• The proposed method utilizes matrix sparseness and approximations to solve issues on computational complexity, sparseness, and scalability arising from hierarchically structured networks.

• We provide analyses on error bounds and complexity to show suitability of the proposed method on semi-supervised learning framework.

• The experimental results show that the proposed algorithms perform well with hierarchically structured data, and, outperform an ordinary semi-supervised learning algorithm.

摘要

•We develop a semi-supervised learning framework of hierarchically structured networks.•The proposed method utilizes matrix sparseness and approximations to solve issues on computational complexity, sparseness, and scalability arising from hierarchically structured networks.•We provide analyses on error bounds and complexity to show suitability of the proposed method on semi-supervised learning framework.•The experimental results show that the proposed algorithms perform well with hierarchically structured data, and, outperform an ordinary semi-supervised learning algorithm.

论文关键词：Hierarchical graph integration,Hierarchical networks,Hierarchically structured networks,Semi-supervised learning

论文评审过程：Received 15 October 2018, Revised 9 April 2019, Accepted 15 June 2019, Available online 16 June 2019, Version of Record 21 June 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.06.009