Robust kernelized graph-based learning
作者：
Highlights：
• A graph-based learning using multiple kernels and multiple views.
• A self-weighted algorithm where each kernel of each view gets an appropriate weight automatically.
• Robustness of the algorithm to reduce the effect of data outliers and noises present in the data set.
• A unified framework for graph construction, kernel learning and label learning.
摘要
•A graph-based learning using multiple kernels and multiple views.•A self-weighted algorithm where each kernel of each view gets an appropriate weight automatically.•Robustness of the algorithm to reduce the effect of data outliers and noises present in the data set.•A unified framework for graph construction, kernel learning and label learning.
论文关键词：Robust,Clustering,Semi-supervised classification,Multiple kernels,Multiple views
论文评审过程：Received 27 November 2019, Revised 24 June 2020, Accepted 30 August 2020, Available online 1 September 2020, Version of Record 2 September 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107628
