Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis
Recently, sparse and low-rank graph-based discriminant analysis (SLGDA) has yielded satisfactory results in hyperspectral image (HSI) dimensionality reduction (DR), for which sparsity and low-rankness are simultaneously imposed to capture both local and global structure of hyperspectral data. Howeve...
Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2017-05-01
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Series: | Remote Sensing |
Subjects: | |
Online Access: | http://www.mdpi.com/2072-4292/9/5/452 |