Name
Title | ||
|---|---|---|
LLE score: a new filter-based unsupervised feature selection method based on nonlinear manifold embedding and its application to image recognition. |
Abstract | ||
|---|---|---|
The task of feature selection is to find the most representative features from the original high-dimensional data. Because of the absence of the information of class labels, selecting the appropriate features in unsupervised learning scenarios is much harder than that in supervised scenarios. In this paper, we investigate the potential of locally linear embedding (LLE), which is a popular manifold... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TIP.2017.2733200 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Feature extraction,Laplace equations,Correlation,Manifolds,Learning systems,Algorithm design and analysis,Face | Data set,Feature selection,Unsupervised learning,Artificial intelligence,Nonlinear dimensionality reduction,Computer vision,Algorithm design,Embedding,Pattern recognition,Feature (computer vision),Feature extraction,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
26 | 11 | 1057-7149 |
Citations | PageRank | References |
17 | 0.59 | 36 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Yao | 1 | 20 | 0.96 |
| Y. F. Liu | 2 | 454 | 30.59 |
| Bo Jiang | 3 | 17 | 0.59 |
| Jungong Han | 4 | 1785 | 117.64 |
| Junwei Han | 5 | 3501 | 194.57 |