Gretton A, Smola A, Bousquet O, Herbrich R, Belitski A, Augath M, Murayama Y, Pauls J, Sch?lkopf B, Logothetis N. Kernel constrained covariance for dependence measurement. In: Proceedings of 10th International Workshop on Artificial Intelligence and Statistics. New Jersey, USA: Society for Artificial Intelligence and Statistics, 2005. 12-23
[2]
Gretton A, Bousquet B, Smola A, Sch?lkopf B. Measuring statistical dependence with Hilbert-Schmidt norms. In: Proceedings of 16th International Conference on Algorithmic Learning Theory. Singapore: Springer, 2005. 63-77
[3]
Bach F R, Jordan M I. Kernel independent component analysis. Journal of Machine Learning Research, 2002, 3: 1-48
[4]
Song L, Smola A, Gretton A, Borgwardt K M. A dependence maximization view of clustering. In: Proceedings of the 24th International Conference on Machine Learning. New York, USA: ACM, 2007. 815-822
[5]
Blaschko M, Gretton A. Learning taxonomies by dependence maximization. In: Proceedings of Advances in Neural Information Processing Systems. Cambridge, Massachusetts, USA: MIT Press, 2008. 153-160
[6]
Zhang Y, Zhou Z H. Multi-label dimensionality reduction via dependency maximization. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence. California, USA: AAAI Press, 2008. 1503-1505
[7]
Gretton A, Fukumizu K, Teo C H, L. Song, Sch?lkopf B, Smola A J. A kernel statistical test of independence. In: Proceedings of Advances in Neural Information Processing Systems. Cambridge, Massachusetts, USA: MIT Press, 2008. 582-592
[8]
Jia Y Q, Nie F P, Zhang C S. Trace ratio problem revisited. IEEE Transactions on Neural Networks, 2009, 20(4): 729-735
[9]
Wang H, Yan S C, Xu D, Tang X O, Huang T. Trace ratio vs. ratio trace for dimensionality reduction. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN: IEEE, 2007. 1-8
[10]
Boutell M R, Luo J B, Shen X P, Brown C M. Learning multi-label scene classification. Pattern Recognition, 2004, 37(9): 1757-1771
[11]
Trohidis K, Tsoumakas G, Kalliris G, Vlahavas I P. Multi-label classification of music into emotions. In: Proceedings of the 9th International Conference on Music Information Retrieval. Philadelphia, USA: Drexel University, 2008. 325-330
[12]
Katakis I, Tsoumakas G, Vlahavas I. Multilabel text classification for automated tag suggestion. In: Proceedings of the ECML/PKDD 2008 Discovery Challenge. Heidelberg, Berlin: Springer, 2008. 75-83
[13]
Lewis D D, Yang Y M, Rose T G, Li F. RCV1: a new benchmark collection for text categorization research. Journal of Machine Learning Research, 2004, 5: 361-397
[14]
Tsoumakas G, Vilcek J, Xioufis E S. Mulan: a java library for multi-label learning [Online], available: http://mulan.sourceforge.net/datasets.html, January 1, 2010
[15]
Schapire R E, Singer Y. Boostexter: a boosting-based system for text categorization. Machine Learning, 2000, 39(2-3): 135-168
[16]
Tsoumakas G, Ioannis K, Ioannis V. Mining multi-label data. Data Mining and Knowledge Discovery Handbook. Berlin: Springer-Verlag, 2010. 667-685
[17]
Zhang M L, Zhou Z H. A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837
[18]
Read J, Pfahringer B, Holmes G, Frank E. Classifier chains for multi-label classification. Machine Learning, 2011, 85(3): 333-359
[19]
Tsoumakas G, Katakis I, Vlahavas I. Random k-labelsets for multi-label classification. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(7): 1079-1089
[20]
Tsoumakas G, Vlahavas I. Random k-labelsets: an ensemble method for multilabel classification. In: Proceedings of the 18th European Conference on Machine Learning. Warsaw, Poland: Springer, 2007. 406-417
[21]
Zhang M L, Zhou Z H. A k-nearest neighbor based algorithm for multi-label classification. In: Proceedings of the 2005 IEEE International Conference on Granular Computing. New York, USA: IEEE, 2005. 718-721
[22]
Elisseeff A, Weston J. A kernel method for multi-labelled classification. In: Proceedings of Advances in Neural Information Processing Systems. Cambridge, Massachusetts, USA: MIT Press, 2001. 681-687
[23]
Zha Z J, Mei T, Wang J D, Wang Z F, Hua X S. Graph-based semi-supervised learning with multiple labels. Journal of Visual Communication and Image Representation, 2009, 20(2): 97-103
[24]
Chen G, Song Y Q, Wang F, Zhang C S. Semi-supervised multi-label learning by solving a Sylvester equation. In: Proceedings of the 2008 SIAM International Conference on Data Mining. Atlanta, USA: Curran Associates, 2008. 410-419
[25]
Li Yu-Feng, Huang Sheng-Jun, Zhou Zhi-Hua. Regularized semi-supervised multi-label learning. Journal of Computer Research and Development, 2012, 49(6): 1272-1278(李宇峰, 黄圣君, 周志华. 一种基于正则化的半监督多标记学习方法. 计算机研究与发展, 2012, 49(6): 1272-1278)
[26]
Wang J D, Zhao Y H, Wu X Q, Hua X S. A transductive multi-label learning approach for video concept detection. Pattern Recognition, 2011, 44(10-11): 2274-2286
[27]
Guo Y H, Schuurmans D. Semi-supervised multi-label classification. In: Proceedings of the 2012 European Conference, Machine Learning and Knowledge Discovery in Databases. Bristol, UK: Springer, 2012. 355-370
[28]
Wu L, Zhang M L. Multi-Label classification with unlabeled data: an inductive approach. In: Proceedings of the 2013 Asian Conference on Machine Learning. Cambridge, Massachusetts, USA: MIT Press/JMLR, 2013. 197-212
[29]
Liu Y, Jin R, Yang L. Semi-supervised multi-label learning by constrained non-negative matrix factorization. In: Proceedings of the 21st National Conference on Artificial Intelligence. California, USA: AAAI Press, 2006. 421-426
[30]
Kong X N, Ng M K, Zhou Z H. Transductive multilabel learning via label set propagation. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(3): 704-719