Review on graph learning for dimensionality reduction of hyperspectral image
| Autor: | Liangpei Zhang, Fulin Luo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Geo-spatial Information Science, Vol 23, Iss 1, Pp 98-106 (2020) |
| Druh dokumentu: | article |
| ISSN: | 1009-5020 1993-5153 10095020 |
| DOI: | 10.1080/10095020.2020.1720529 |
| Popis: | Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction for a hyperspectral image. Firstly, we review the development of graph learning and its application in a hyperspectral image. Then, we mainly discuss several representative graph methods including two manifold learning methods, two sparse graph learning methods, and two hypergraph learning methods. For manifold learning, we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph. For sparse graph, we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph. For hypergraph learning, we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data. |
| Databáze: | Directory of Open Access Journals |
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