Name
Abstract | ||
|---|---|---|
In this brief, we propose a new max-margin-based discriminative feature learning method. In particular, we aim at learning a low-dimensional feature representation, so as to maximize the global margin of the data and make the samples from the same class as close as possible. In order to enhance the robustness to noise, we leverage a regularization term to make the transformation matrix sparse in r... |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TNNLS.2016.2520099 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Correlation,Support vector machines,Linear programming,Learning systems,Covariance matrices,Data visualization,Manifolds | Data visualization,Pattern recognition,Computer science,Support vector machine,Robustness (computer science),Regularization (mathematics),Artificial intelligence,Linear programming,Transformation matrix,Discriminative model,Machine learning,Feature learning | Journal |
Volume | Issue | ISSN |
27 | 12 | 2162-237X |
Citations | PageRank | References |
6 | 0.41 | 27 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changsheng Li | 1 | 108 | 9.64 |
| QingShan Liu | 2 | 2625 | 162.58 |
| Weishan Dong | 3 | 19 | 3.13 |
| Wei Fan | 4 | 4205 | 253.58 |
| Xin Zhang | 5 | 17 | 1.88 |
| Lin Yang | 6 | 17 | 2.77 |