Name
Abstract | ||
|---|---|---|
In this paper, we propose a multiple-metric learning algorithm to learn jointly a set of optimal homogenous/heterogeneous metrics in order to fuse the data collected from multiple sensors for joint classification. The learned metrics have the potential to perform better than the conventional Euclidean metric for classification. Moreover, in the case of heterogenous sensors, the learned multiple metrics can be quite different, which are adapted to each type of sensor. By learning the multiple metrics jointly within a single unified optimization framework, we can learn better metrics to fuse the multi-sensor data for a joint classification. Furthermore, we also exploit multi-metric learning in a kernel induced feature space to capture the non-linearity in the original feature space via kernel mapping. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.inffus.2012.05.002 | Information Fusion |
Keywords | Field | DocType |
better metrics,multi-sensor data,multi-metric learning,multiple metrics,heterogeneous metrics,kernel induced feature space,original feature space,multi-sensor fusion,joint classification,multiple sensor,kernel mapping | Data mining,Artificial intelligence,Fuse (electrical),Kernel (linear algebra),Feature vector,Kernel mapping,Pattern recognition,Euclidean distance,Exploit,Sensor fusion,Multiple sensors,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
14 | 4 | 1566-2535 |
Citations | PageRank | References |
10 | 0.48 | 24 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanning Zhang | 1 | 1613 | 176.32 |
| Haichao Zhang | 2 | 487 | 21.41 |
| N. M. Nasrabadi | 3 | 2986 | 372.56 |
| Thomas S. Huang | 4 | 27815 | 2618.42 |