Multi-Instance Multi-Label Learning with Weak Label
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1862-1867.
Abstract
Multi-Instance Multi-Label learning (MIML) deals with data objects that are represented by a bag of instances and associated with a set of class labels simultaneously. Previous studies typically assume that for every training example, all positive labels are tagged whereas the untagged labels are all negative. In many real applications such as image annotation, however, the learning problem often suffers from weak label; that is, users usually tag only a part of positive labels, and the untagged labels are not necessarily negative. In this paper, we propose the MIMLwel approach which works by assuming that highly relevant labels share some common instances, and the underlying class means of bags for each label are with a large margin. Experiments validate the effectiveness of MIMLwel in handling the weak label problem.
@inproceedings{ijcai2013p275,
title = {Multi-Instance Multi-Label Learning with Weak Label},
author = {Yang, Shu-Jun and Jiang, Yuan and Zhou, Zhi-Hua},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {1862--1867},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/275},
}
TY - CPAPER TI - Multi-Instance Multi-Label Learning with Weak Label AU - Yang, Shu-Jun AU - Jiang, Yuan AU - Zhou, Zhi-Hua T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 1862 EP - 1867 UR - https://ijcai.org/proceedings/2013/275 ER -Copied
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