Joint graph optimization and projection learning for dimensionality reduction
作者：
Highlights：
• A novel framework termed joint graph optimization and projection learning (JGOPL) is proposed for graph-based dimensionality reduction.
• The l21-norm based distance measurement is adopted in the loss function of our JGOPL so that its robustness to the negative influence caused by the outliers or variations of data can be improved.
• In order to well exploit and preserve the local structure information of high-dimensional data, a locality constraint is introduced into the proposed JGOPL to discourage a sample from connecting with the distant samples during graph optimization.
• The locality constraint and graph optimization strategy proposed is not only limited to dimensionality reduction, but also can be incorporated into other relevant graph-based tasks.
摘要
•A novel framework termed joint graph optimization and projection learning (JGOPL) is proposed for graph-based dimensionality reduction.•The l21-norm based distance measurement is adopted in the loss function of our JGOPL so that its robustness to the negative influence caused by the outliers or variations of data can be improved.•In order to well exploit and preserve the local structure information of high-dimensional data, a locality constraint is introduced into the proposed JGOPL to discourage a sample from connecting with the distant samples during graph optimization.•The locality constraint and graph optimization strategy proposed is not only limited to dimensionality reduction, but also can be incorporated into other relevant graph-based tasks.
论文关键词：Graph optimization,Projection learning,Dimensionality reduction,Robustness
论文评审过程：Received 28 September 2018, Revised 31 January 2019, Accepted 23 March 2019, Available online 23 March 2019, Version of Record 9 April 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.03.024
