[1]
X. Geng, N. Xu, and R. Shao, Label enhancement for label distribution learning, Journal of Computer Research and Development, vol. 54, no. 6, pp. 1171-1184, 2017.
[2]
Z. Wang, J. Xin, H. Yang, S. Tian, G. Yu, C. Xu, and Y. Yao, Distributed and weighted extreme learning machine for imbalanced big data learning, Tsinghua Science and Technology, vol. 22, no. 2, pp. 160-173, 2017.
[3]
K. Wang, M. Yang, W. Yang, and Y. Yin, Deep cross-view label embedding with correlation and structure preserved for multi-label classification, in Proc. of International Conference on Tools with Artificial Intelligence, Volos, Greece, 2018, pp. 12-19.
[4]
F. Tai and H. Lin, Multilabel classification with principal label space transformation, Neural Computation, vol. 24, no. 9, pp. 2508-2542, 2012.
[5]
L. Sun, S. Ji, and J. Ye, Canonical correlation analysis for multilabel classification: A least-squares formulation, extensions, and analysis, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 33, no. 1, pp. 194-200, 2011.
[6]
D. Tuia, J. Verrelst, L. Alonso, F. Prez-Cruz, and G. Camps-Valls, Multioutput support vector regression for remote sensing biophysical parameter estimation, Geoscience and Remote Sensing Letters, vol. 8, no. 4, pp. 804-808, 2011.
[7]
X. Geng, Label distribution learning, IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 7, pp. 1734-1748, 2016.
[8]
P. Hou, X. Geng, and M. Zhang, Multi-label manifold learning, in Proc. of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 2016, pp. 1680-1686.
[9]
X. Zhu, J. Lafferty, and R. Rosenfeld, Semi-supervised learning with graphs, PhD dissertation, Dept. Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA, 2005.
[10]
N. Xu, A. Tao, and X. Geng, Label enhancement for label distribution learning, in Proc. of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018, pp. 2926-2932.
[11]
C. Peng, A. Tao, and X. Geng, Label embedding based on multi-scale locality preservation, in Proc. of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018, pp. 2623-2629.
[12]
R. Shao, N. Xu, and X. Geng, Multi-label learning with label enhancement, in Proc. of IEEE International Conference on Data Mining, Singapore, 2018, pp. 437-446.
[13]
Z. Zhang and H. Zha, Principal manifolds and nonlinear dimensionality reduction via tangent space alignment, SIAM Journal of Scientific Computing, vol. 26, no. 1, pp. 313-338, 2004.
[14]
Y. Jiang, Z. Deng, J. Wang, P. Qian, and S. Wang, Collaborative partition multi-view fuzzy clustering algorithm using entropy weighting, Journal of Software, vol. 25, no. 10, pp. 2293-2311, 2014.
[15]
M. Zhang and Z. Zhou, Multi-label neural networks with applications to functional genomics and text categorization, IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 10, pp. 1338-1351, 2006.
[16]
X. Geng and P. Hou, Pre-release prediction of crowd opinion on movies by label distribution learning, in Proc. of the 24th International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, 2015, pp. 3511-3517.
[17]
X. Geng, C. Yin, and Z. Zhou, Facial age estimation by learning from label distributions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 10, pp. 2401-2412, 2013.
[18]
M. Zhang and Z. Zhou, A review on multi-label learning algorithms, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 8, pp. 1819-1837, 2014.
[19]
J. Demsar, Statistical comparisons of classifiers over multiple datasets, Journal of Machine Learning Research, vol. 7, no. 1, pp. 1-30, 2006.
