Name
Abstract | ||
|---|---|---|
•propose a non-parametric metric learning approach (GP-Metric) based on Gaussian Process (GP).•use GP to extend the bilinear similarity into a non-parametric form.•develop an efficient algorithm to learn the non-parametric metric.•demonstrate the performance of GP-Metric on real-world datasets. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2018.10.010 | Pattern Recognition |
Keywords | Field | DocType |
Metric learning,Gaussian process,Bilinear similarity,Non-parametric metric | Feature vector,Nonlinear system,Metric (mathematics),Parametric statistics,Artificial intelligence,Gaussian process,Overfitting,Feature learning,Mathematics,Machine learning,Bilinear interpolation | Journal |
Volume | Issue | ISSN |
87 | 1 | 0031-3203 |
Citations | PageRank | References |
0 | 0.34 | 32 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Li | 1 | 78 | 14.22 |
| Songcan Chen | 2 | 4148 | 191.89 |