Semisupervised Manifold Joint Hypergraphs for Dimensionality Reduction of Hyperspectral Image

In this letter, a new semisupervised dimensionality reduction (DR) method, termed geodesic-based manifold joint hypergraphs (GMJHs), is proposed for hyperspectral image (HSI). This method first builds a geodesic-based reconstruction model to discover the nonlinear similarity between two manifold rec...

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Veröffentlicht in:	IEEE geoscience and remote sensing letters 2021-10, Vol.18 (10), p.1811-1815
Hauptverfasser:	Duan, Yule, Huang, Hong, Tang, Yuxiao, Li, Yuan, Pu, Chunyu
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Dimensionality reduction Dimensionality reduction (DR) Direct reduction Embedding geodesic distance Graph theory Graphs hypergraph framework hyperspectral image (HSI) Hyperspectral imaging Linear programming manifold learning Manifolds Manifolds (mathematics) Probabilistic logic Reconstruction Training
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