Name
Abstract | ||
|---|---|---|
Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1142/S012906571750040X | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
Distance metric learning, semi-supervised learning, intrinsic algorithm, multiple kernel, nearest neighbor | Chebyshev distance,Equivalence of metrics,Semi-supervised learning,Metric k-center,Computer science,Metric (mathematics),Intrinsic metric,Artificial intelligence,Mathematical optimization,Fisher information metric,Pattern recognition,String metric,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 2 | 0129-0657 |
Citations | PageRank | References |
2 | 0.36 | 34 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Li | 1 | 2 | 0.70 |
| Yanqin Bai | 2 | 4 | 2.43 |
| Yaxin Peng | 3 | 73 | 16.82 |
| Shaoyi Du | 4 | 357 | 40.68 |
| Shihui Ying | 5 | 233 | 23.32 |