Multi-Kernel Multi-Label Learning with Max-Margin Concept Network
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1615-1619.
Abstract
In this paper, a novel method is developed for enabling Multi-Kernel Multi-Label Learning. Inter-label dependency and similarity diversity are simultaneously leveraged in the proposed method. A concept network is constructed to capture the inter-label correlations for classifier training. Maximal margin approach is used to effectively formulate the feature-label associations and the label-label correlations. Specific kernels are learned not only for each label but also for each pair of the inter-related labels. By learning the eigenfunctions of the kernels, the similarity between a new data point and the training samples can be computed in the online mode. Our experimental results on real datasets (web pages, images, music, and bioinformatics) have demonstrated the effectiveness of our method.
@inproceedings{ijcai2011p271,
title = {Multi-Kernel Multi-Label Learning with Max-Margin Concept Network},
author = {Zhang, Wei and Xue, Xiangyang and Fan, Jianping and Huang, Xiaojing and Wu, Bin and Liu, Mingjie},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1615--1619},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-271},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-271},
}
TY - CPAPER TI - Multi-Kernel Multi-Label Learning with Max-Margin Concept Network AU - Zhang, Wei AU - Xue, Xiangyang AU - Fan, Jianping AU - Huang, Xiaojing AU - Wu, Bin AU - Liu, Mingjie T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1615 EP - 1619 DO - 10.5591/978-1-57735-516-8/IJCAI11-271 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-271 ER -Copied
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