Incomplete multi-view weak-label learning
conference contribution
posted on 2018-01-01, 00:00 authored by Q Tan, G Yu, C Domeniconi, J Wang, Zili ZhangZili Zhang© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. Learning from multi-view multi-label data has wide applications. Two main challenges characterize this learning task: incomplete views and missing (weak) labels. The former assumes that views may not include all data objects. The weak label setting implies that only a subset of relevant labels are provided for training objects while other labels are missing. Both incomplete views and weak labels can lead to significant performance degradation. In this paper, we propose a novel model (iMVWL) to jointly address the two challenges. iMVWL learns a shared subspace from incomplete views with weak labels, local label correlations, and a predictor in this subspace, simultaneously. The latter can capture not only cross-view relationships but also weak-label information of training samples. We further develop an alternative solution to optimize our model; this solution can avoid suboptimal results and reinforce their reciprocal effects, and thus further improve the performance. Extensive experimental results on real-world datasets validate the effectiveness of our model against other competitive algorithms.
History
Pagination
2703-2709Location
Stockholm, SwedenStart date
2018-07-13End date
2018-07-19ISSN
1045-0823ISBN-13
9780999241127Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, International Joint Conferences on Artificial IntelligenceEditor/Contributor(s)
Lang JTitle of proceedings
IJCAI 2018: Proceedings of the 27th International Joint Conference on Artificial IntelligenceEvent
Artificial Intelligence. International Joint Conference (27th : 2018 : Stockholm, Sweden)Publisher
International Joint Conferences on Artificial IntelligencePlace of publication
Vienna, AustriaPublication URL
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