Name
Abstract | ||
|---|---|---|
Regression based on hyperspectral remote sensing data contains two-fold complications, i.e., lack of labeled data and difficulty in collecting quantitative ground-truth. In this paper, we propose semi-supervised subspace learning methods for regression based on a generalized eigenvalue problem. The methods exploit abundant unlabeled data for low-dimensional subspace learning. Quantitative target values are replaced by ordinal values that can be easily acquired in comparison with accurate quantitative ground-truth. The subspace learning methods are further expanded into nonlinear manifold learning methods by the kernel trick. The methods are applied to estimation problems of growth-state-related properties of rice based on hyprspectral remote sensing data. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IGARSS.2015.7325684 | 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) |
Keywords | Field | DocType |
Hyperspectral data,dimensionality reduction,manifold learning,order constraints,semi-supervised learning,generalized eigenvalue problem,kernelization | Semi-supervised learning,Computer science,Manifold alignment,Artificial intelligence,Nonlinear dimensionality reduction,Kernel (linear algebra),Computer vision,Subspace topology,Pattern recognition,Hyperspectral imaging,Eigendecomposition of a matrix,Kernel method,Machine learning | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuniaki Uto | 1 | 32 | 10.40 |
| Yukio Kosugi | 2 | 127 | 26.67 |
| Genya Saito | 3 | 44 | 7.04 |