Semi-supervised dimensionality reduction of hyperspectral imagery using pseudo-labels
作者：
Highlights：
• A new semi-supervised feature reduction approach is proposed.
• Pseudo-labels are generated using Dirichlet Process Mixing Model, which are then used for learning a semi-supervised dimensionality reduction projection that simultaneously preserves locality.
• Projection is undertaken in both raw and kernel space.
• Results with hyperspectral data demonstrates substantial improvement in performance.
摘要
•A new semi-supervised feature reduction approach is proposed.•Pseudo-labels are generated using Dirichlet Process Mixing Model, which are then used for learning a semi-supervised dimensionality reduction projection that simultaneously preserves locality.•Projection is undertaken in both raw and kernel space.•Results with hyperspectral data demonstrates substantial improvement in performance.
论文关键词：Dimensionality reduction,Semi-supervised learning,Dirichlet process mixture model,Hyperspectral data classification
论文评审过程：Received 1 July 2016, Revised 2 June 2017, Accepted 4 September 2017, Available online 12 September 2017, Version of Record 26 September 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.09.003
