Name
Abstract | ||
|---|---|---|
Feature selection is often required to select a feature subset from the original feature set of objects of very high resolution (VHR) remote sensing images. However, the majority of feature selection methods is supervised, and could fail to identify the relevant features when labeled objects are scarce. To address the problem, this paper proposes a method, efficient semi-supervised feature selection (ESFS), by effectively exploiting the underlying information of the huge amount of unlabeled objects. Firstly, probability matrix of unlabeled objects is utilized in loss function to measure the relevance of features on classes, instead of using traditional graph. Secondly, construction a l (1,2) -norm regularization term is imposed to ensure the sparsity in rows of the selection matrix, and consequent feature selection. Experiments are carried on a VHR image demonstrate that ESFS outperforms other classical and latest methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7729383 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
Semi-supervised features selection, classification, l(2),(1) -norm regularization | Feature selection,Matrix (mathematics),Computer science,Remote sensing,Regularization (mathematics),Artificial intelligence,Row,Computer vision,Pattern recognition,Feature (computer vision),Support vector machine,Feature extraction,Image resolution | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Chen | 1 | 3 | 1.13 |
| Lin Song | 2 | 0 | 0.34 |
| Yuguan Hou | 3 | 2 | 2.40 |
| Guofan Shao | 4 | 50 | 6.84 |