Name
Title | ||
|---|---|---|
Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning. |
Abstract | ||
|---|---|---|
This paper presents a kernel-based feature selection method for the classification of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant (discriminant) for the considered classification problem, i.e., preserve the functional relationship between input and output variables, and 2) invariant (stable) across different domains, i.e.,... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TGRS.2015.2503885 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Hyperspectral imaging,Kernel,Feature extraction,Estimation,Search problems,Support vector machines | Graph kernel,Radial basis function kernel,Pattern recognition,Kernel embedding of distributions,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,Kernel method,Variable kernel density estimation,Reproducing kernel Hilbert space,Mathematics | Journal |
Volume | Issue | ISSN |
54 | 5 | 0196-2892 |
Citations | PageRank | References |
16 | 0.66 | 37 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Claudio Persello | 1 | 237 | 20.33 |
| Lorenzo Bruzzone | 2 | 4952 | 387.72 |