Name
Title | ||
|---|---|---|
Local Adaptive Projection Framework for Feature Selection of Labeled and Unlabeled Data. |
Abstract | ||
|---|---|---|
Most feature selection methods first compute a similarity matrix by assigning a fixed value to pairs of objects in the whole data or to pairs of objects in a class or by computing the similarity between two objects from the original data. The similarity matrix is fixed as a constant in the subsequent feature selection process. However, the similarities computed from the original data may be unreli... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2018.2830186 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Feature extraction,Sparse matrices,Robustness,Laplace equations,Learning systems,Principal component analysis,Computer science | Data set,Pattern recognition,Feature selection,Computer science,Iterative method,Projection (linear algebra),Robustness (computer science),Feature extraction,Artificial intelligence,Sparse matrix,Principal component analysis | Journal |
Volume | Issue | ISSN |
29 | 12 | 2162-237X |
Citations | PageRank | References |
7 | 0.41 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaojun Chen | 1 | 1298 | 107.51 |
| Guowen Yuan | 2 | 26 | 3.05 |
| Wenting Wang | 3 | 233 | 25.66 |
| Feiping Nie | 4 | 7061 | 309.42 |
| Xiaojun Chang | 5 | 1585 | 76.85 |
| Joshua Zhexue Huang | 6 | 20 | 4.39 |