TY - GEN
T1 - Ordinal-based metric learning for learning using privileged information
AU - Fouad, Shereen
AU - Tino, Peter
PY - 2014/1/9
Y1 - 2014/1/9
N2 - Learning Using privileged Information (LUPI), originally proposed in [1], is an advanced learning paradigm that aims to improve the supervised learning in the presence of additional (privileged) information, available during training, but not in the test phase. We present a novel metric learning methodology that is specially designed for incorporating privileged information in ordinal classification tasks, where there is a natural order on the set of classes. This is done by changing the global metric in the input space, based on distance relations revealed by the privileged information. The proposed model is formulated in the context of ordinal prototype based classification with metric adaptation. Unlike the existing nominal version of LUPI in prototype models [8], [9], in ordinal classifications the proposed LUPI model takes explicitly into account the class order information during the input space metric learning. Experiments demonstrate that incorporating privileged information via the proposed ordinal-based metric learning can improve the ordinal classification performance.
AB - Learning Using privileged Information (LUPI), originally proposed in [1], is an advanced learning paradigm that aims to improve the supervised learning in the presence of additional (privileged) information, available during training, but not in the test phase. We present a novel metric learning methodology that is specially designed for incorporating privileged information in ordinal classification tasks, where there is a natural order on the set of classes. This is done by changing the global metric in the input space, based on distance relations revealed by the privileged information. The proposed model is formulated in the context of ordinal prototype based classification with metric adaptation. Unlike the existing nominal version of LUPI in prototype models [8], [9], in ordinal classifications the proposed LUPI model takes explicitly into account the class order information during the input space metric learning. Experiments demonstrate that incorporating privileged information via the proposed ordinal-based metric learning can improve the ordinal classification performance.
UR - http://www.scopus.com/inward/record.url?scp=84893549968&partnerID=8YFLogxK
UR - https://ieeexplore.ieee.org/document/6706799
U2 - 10.1109/IJCNN.2013.6706799
DO - 10.1109/IJCNN.2013.6706799
M3 - Conference publication
AN - SCOPUS:84893549968
SN - 9781467361293
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2013 International Joint Conference on Neural Networks, IJCNN 2013
PB - IEEE
T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013
Y2 - 4 August 2013 through 9 August 2013
ER -