Ordinal-based metric learning for learning using privileged information

Shereen Fouad, Peter Tino

Research output: Chapter in Book/Published conference outputConference publication

Abstract

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.

Original languageEnglish
Title of host publication2013 International Joint Conference on Neural Networks, IJCNN 2013
PublisherIEEE
ISBN (Print)9781467361293
DOIs
Publication statusPublished - 9 Jan 2014
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
Duration: 4 Aug 20139 Aug 2013

Publication series

NameProceedings of the International Joint Conference on Neural Networks
PublisherIEEE
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
Country/TerritoryUnited States
CityDallas, TX
Period4/08/139/08/13

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