Local distances preserving based manifold learning
作者：
Highlights：
• Defining a local distances preserving based manifold learning method.
• Defining a neighborhood graph matrix by better description of the local distances.
• Defining a new cost function for graph matrix to find the embedded data manifold.
• Low sensitivity to the initialization of the parameters.
• Defining a criterion to evaluate the local manifold learning methods.
摘要
•Defining a local distances preserving based manifold learning method.•Defining a neighborhood graph matrix by better description of the local distances.•Defining a new cost function for graph matrix to find the embedded data manifold.•Low sensitivity to the initialization of the parameters.•Defining a criterion to evaluate the local manifold learning methods.
论文关键词：Dimension reduction,Manifold learning,Euclidean distance,Recognition rate,Local distance
论文评审过程：Received 29 September 2018, Revised 11 July 2019, Accepted 2 August 2019, Available online 3 August 2019, Version of Record 9 August 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.112860
