Name
Title | ||
|---|---|---|
Kernel Low-Rank Multitask Learning in Variational Mode Decomposition Domain for Multi-/Hyperspectral Classification. |
Abstract | ||
|---|---|---|
Multitask learning (MTL) has recently yielded impressive results for classification of remotely sensed data due to its ability to incorporate shared information across multiple tasks. However, it remains a challenging issue to achieve robust classification results in the case that the data are from nonlinear subspaces. In this paper, we propose a kernel low-rank MTL (KL-MTL) method to handle multi... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TGRS.2018.2828612 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Feature extraction,Kernel,Task analysis,Hyperspectral sensors,Support vector machines,Optimization | Kernel (linear algebra),Computer vision,Multi-task learning,Pattern recognition,Support vector machine,Hyperspectral imaging,Feature extraction,Augmented Lagrangian method,Artificial intelligence,Optimization problem,Mathematics,Hilbert–Huang transform | Journal |
Volume | Issue | ISSN |
56 | 7 | 0196-2892 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
5 | ||