Name
Title | ||
|---|---|---|
Feature weight estimation based on dynamic representation and neighbor sparse reconstruction. |
Abstract | ||
|---|---|---|
•We propose a new dynamic representation framework for feature weight estimation, which redefines the optimization problem.•Using gradient ascent method, we provide an effective method to solve the optimization problem of DRNSR-Relief and can guarantee its convergence.•A novel neighbor sparse reconstruction method is proposed for represent neighbors of the given samples. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patcog.2018.03.014 | Pattern Recognition |
Keywords | Field | DocType |
Feature weighting,Feature selection,Relief,Sparse learning,Local hyperplane,l1 regularization,Classification | Convergence (routing),Feature vector,Gradient descent,Feature selection,Pattern recognition,Weight,Supervised learning,Regularization (mathematics),Artificial intelligence,Hyperplane,Mathematics | Journal |
Volume | Issue | ISSN |
81 | 1 | 0031-3203 |
Citations | PageRank | References |
1 | 0.36 | 23 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaojuan Huang | 1 | 14 | 1.85 |
| Li Zhang | 2 | 363 | 39.03 |
| bangjun wang | 3 | 5 | 1.43 |
| Zhao Zhang | 4 | 938 | 65.99 |
| Fan-Zhang Li | 5 | 167 | 19.56 |