Article

Wang, Jing, Tian, Feng, Yu, Hongchuan, Liu, Chang Hong, Zhan, Kun and Wang, Xiao (2017) Diverse non-negative matrix factorization for multiview data representation. IEEE Transactions on Cybernetics, 48 (9). pp. 2620-2632. ISSN 2168-2267 (Print), 2168-2275 (Online) (doi:https://doi.org/10.1109/TCYB.2017.2747400)

Zhan, Kun, Shi, Jinhui, Wang, Jing and Tian, Feng (2017) Graph-regularized concept factorization for multi-view document clustering. Journal of Visual Communication and Image Representation, 48. pp. 411-418. ISSN 1047-3203 (doi:https://doi.org/10.1016/j.jvcir.2017.02.019)

Wang, Jing, Wang, Xiao, Tian, Feng, Liu, Chang Hong and Yu, Hongchuan (2016) Constrained low-rank representation for robust subspace clustering. IEEE Transactions on Cybernetics, 47 (12). pp. 4534-4546. ISSN 2168-2267 (Print), 2168-2275 (Online) (doi:https://doi.org/10.1109/TCYB.2016.2618852)

Conference Proceedings

Suzuki, Atsushi, Nitanda, Atsushi, Suzuki, Taiji, Wang, Jing, Tian, Feng and Yamanishi, Kenji (2023) Tight and fast generalization error bound of graph embedding in metric space. In: Proceedings of the 40th International Conference on Machine Learning. Volume 202: International Conference on Machine Learning, 23rd - 29th July 2023, Honolulu, Hawaii, USA. Proceedings of Machine Learning Research (PMLR) Press - Journal of Machine Learning Research (JMLR), Cambridge MA, USA, pp. 33268-33284. ISSN 1938-7228 (Print), 2640-3498 (Online)

Suzuki, Atsushi, Wang, Jing, Tian, Feng, Nitanda, Atsushi and Yamanishi, Kenji (2019) Hyperbolic ordinal embedding. In: Asian Conference on Machine Learning, 17-19 November 2019, Nagoya, Japan. Proceedings of Machine Learning Research, 101 . MIR, Moscow, Russia, pp. 1065-1080.

Wang, Jing, Xu, Linchuan, Tian, Feng, Suzuki, Atsushi, Zhang, Changqing and Yamanishi, Kenji (2019) Attributed subspace clustering. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, pp. 3719-3725. ISBN 978-0999241141 (doi:https://doi.org/10.24963/ijcai.2019/516)

Wang, Jing, Suzuki, Atsushi, Xu, Linchuan, Tian, Feng, Yang, Liang and Yamanishi, Kenji (2019) Orderly subspace clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press, Palo Alto, California USA, pp. 5264-5272. ISBN 978-1577358091 ISSN 2159-5399 (Print), 2374-3468 (Online) (doi:https://doi.org/10.1609/aaai.v33i01.33015264)

Wang, Jing, Tian, Feng, Liu, Weiwei, Wang, Xiao, Zhang, Wenjie and Yamanishi, Kenji (2018) Ranking preserving nonnegative matrix factorization. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, pp. 2776-2782. ISBN 978-0999241127 (doi:https://doi.org/10.24963/ijcai.2018/385)

Wang, Jing, Tian, Feng, Wang, Xiao, Yu, Hongchuan, Liu, Chang Hong and Yang, Liang (2017) Multi-component nonnegative matrix factorization. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, pp. 2922-2928. ISBN 978-0999241103 (doi:https://doi.org/10.24963/ijcai.2017/407)

Wang, Jing, Tian, Feng, Liu, Chang Hong, Yu, Hongchuan, Wang, Xiao and Tang, Xianchao (2017) Robust nonnegative matrix factorization with ordered structure constraints. In: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 478-485. ISBN 978-1509061839 ISSN 2161-4407 (Online) (doi:https://doi.org/10.1109/IJCNN.2017.7965892)

Wang, Jing, Wang, Xiao, Tian, Feng, Liu, Chang Hong, Yu, Hongchuan and Liu, Yanbei (2016) Adaptive multi-view semi-supervised nonnegative matrix factorization. In: ICONIP 2016: Neural Information Processing. Lecture Notes in Computer Science, 9948 . Springer, pp. 435-444. ISBN 978-3319466712 ISSN 0302-9743 (Print), 1611-3349 (Online) (doi:https://doi.org/10.1007/978-3-319-46672-9_49)

Wang, Jing, Tian, Feng, Liu, Chang Hong and Wang, Xiao (2015) Robust semi-supervised nonnegative matrix factorization. In: 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8. ISBN 978-1479919604 ISSN 2161-4393 (Print), 2161-4407 (Online) (doi:https://doi.org/10.1109/IJCNN.2015.7280422)