Random Multi-Graphs: A semi-supervised learning framework for classification of high dimensional data
作者：
Highlights：
• A novel graph-based semi-supervised learning framework is proposed.
• RMG can handle high dimensional problems by injecting randomness into the graph.
• Randomness as a regularization can avoid curse of dimensionality and overfitting.
• Experimental results on eight data sets are presented to show the effectiveness.
摘要
•A novel graph-based semi-supervised learning framework is proposed.•RMG can handle high dimensional problems by injecting randomness into the graph.•Randomness as a regularization can avoid curse of dimensionality and overfitting.•Experimental results on eight data sets are presented to show the effectiveness.
论文关键词：Semi-supervised learning,Graph,Regularization,Randomness,Anchors 2010 MSC: 00-01, 99-00
论文评审过程：Received 14 May 2016, Revised 22 July 2016, Accepted 16 August 2016, Available online 26 August 2016, Version of Record 22 March 2017.
论文官网地址：https://doi.org/10.1016/j.imavis.2016.08.006
