Learning a Robust Local Manifold Representation for Hyperspectral Dimensionality Reduction
Local manifold learning has been successfully applied to hyperspectral dimensionality reduction in order to embed nonlinear and nonconvex manifolds in the data. Local manifold learning is mainly characterized by affinity matrix construction, which is composed of two steps: neighbor selection and com...
Main Authors: | Danfeng Hong, Naoto Yokoya, Xiao Xiang Zhu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2017-01-01
|
Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/7985008/ |
Similar Items
-
Spectral-Locational-Spatial Manifold Learning for Hyperspectral Images Dimensionality Reduction
by: Na Li, et al.
Published: (2021-07-01) -
A Study on Dimensionality Reduction and Parameters for Hyperspectral Imagery Based on Manifold Learning
by: Wenhui Song, et al.
Published: (2024-03-01) -
Hyperspectral Image Dimensionality Reduction Algorithm Based on Spatial–Spectral Adaptive Multiple Manifolds
by: Shufang Xu, et al.
Published: (2023-08-01) -
Local Geometric Structure Feature for Dimensionality Reduction of Hyperspectral Imagery
by: Fulin Luo, et al.
Published: (2017-08-01) -
Unsupervised Dimensionality Reduction With Multifeature Structure Joint Preserving Embedding for Hyperspectral Imagery
by: Kai Chen, et al.
Published: (2023-01-01)