Name
Abstract | ||
|---|---|---|
The graph embedding (GE) algorithms have been widely applied for dimensionality reduction (DR) of hyperspectral image (HSI). However, a major challenge of GE is unclear how to select the neighborhood size and define the affinity weight. In this paper, we propose a new sparse manifold learning method, called sparse manifold preserving (SMP), for HSI classification. It constructs the affinity weight using the sparse coefficients which reserves the global sparsity and manifold structure of HSI data, while it doesn't need to choose any model parameters for the similarity graph. Experiments on PaviaU HSI data set demonstrate the effectiveness of the presented SMP algorithm. |
Year | Venue | Keywords |
|---|---|---|
2014 | Communications in Computer and Information Science | Hyperspectral image classification,dimensionality reduction,graph embedding,sparse representation,manifold learning |
Field | DocType | Volume |
Hyperspectral image classification,Graph,Dimensionality reduction,Pattern recognition,Graph embedding,Computer science,Sparse approximation,Hyperspectral imaging,Artificial intelligence,Nonlinear dimensionality reduction,Manifold | Conference | 483 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hong Huang | 1 | 78 | 7.57 |
| Fulin Luo | 2 | 34 | 5.85 |
| Jiamin Liu | 3 | 44 | 3.88 |
| Zezhong Ma | 4 | 0 | 0.34 |