TY - GEN
T1 - Heterogeneous multi-metric learning for multi-sensor fusion
AU - Zhang, Haichao
AU - Nasrabadi, Nasser M.
AU - Huang, Thomas S.
AU - Zhang, Yanning
PY - 2011
Y1 - 2011
N2 - 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 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 joint classification.
AB - 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 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 joint classification.
KW - Metric learning
KW - Multi-sensor fusion
UR - http://www.scopus.com/inward/record.url?scp=80052541339&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052541339&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:80052541339
SN - 9781457702679
T3 - Fusion 2011 - 14th International Conference on Information Fusion
BT - Fusion 2011 - 14th International Conference on Information Fusion
T2 - 14th International Conference on Information Fusion, Fusion 2011
Y2 - 5 July 2011 through 8 July 2011
ER -