Name
Abstract | ||
|---|---|---|
This paper analyses the performance of combining Support Vector Machines (SVMs) and metric learning, in order to evaluate the effect of metric learning on improving SVM. First, we establish the sufficient condition under which the performance of SVM cannot be improved by metric learning. Second, to verify whether the sufficient condition holds, we develop a two-step metric learning strategy by learning an orthonormal matrix and a diagonal matrix respectively. Third, we analyze the case when the sufficient condition holds after the two-step metric learning, and therefore demonstrate the practicability of improving the accuracy of SVM. Finally, we provide some experiments, and also apply metric learning into SVM for 3D object classification and face recognition. The experimental results demonstrate the effectiveness of improving the SVM classification performance by metric learning. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s11063-017-9771-7 | Neural Processing Letters |
Keywords | Field | DocType |
Distance metric learning,kNN,SVM,Classification | Facial recognition system,Orthogonal matrix,Pattern recognition,Support vector machine,Artificial intelligence,Diagonal matrix,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
48 | 3 | 1370-4621 |
Citations | PageRank | References |
2 | 0.36 | 25 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingfang Hu | 1 | 2 | 0.69 |
| Juan Hu | 2 | 2 | 0.36 |
| Zhen Ye | 3 | 2 | 0.69 |
| Chaomin Shen | 4 | 6 | 2.09 |
| Yaxin Peng | 5 | 73 | 16.82 |