Multi-view representation learning, Subspace clustering, Low-rank tensor, Constraint matrix

Zhang, Changqing ORCID: https://orcid.org/0000-0003-1410-6650, Fu, Huazhu, Wang, Jing, Li, Wen, Cao, Xiaochun and Hu, Qinghua (2020) Tensorized multi-view subspace representation learning. International Journal of Computer Vision, 128 (8-9). pp. 2344-2361. ISSN 0920-5691 (Print), 1573-1405 (Online) (doi:10.1007/s11263-020-01307-0)

community detection, semi-supervised learning

Yang, Liang, Ge, Meng, Jin, Di, He, Dongxiao, Fu, Huazhu, Wang, Jing and Cao, Xiaochun (2017) Exploring the roles of cannot-link constraint in community detection via multi-variance mixed Gaussian generative model. PLoS One, 12 (7):e0178029. ISSN 1932-6203 (Online) (doi:10.1371/journal.pone.0178029)

community detection, topology information, global information, local information, nonnegative matrix factorization

Tang, Xianchao, Xu, Tao, Feng, Xia, Yang, Guoqing, Wang, Jing, Li, Qiannan, Liu, Yanbei and Wang, Xiao (2017) Learning community structures: global and local perspectives. Neurocomputing, 239. pp. 249-256. ISSN 0925-2312 (doi:10.1016/j.neucom.2017.02.026)

diversity representation, multiview learning, non-negative matrix factorization (NMF)

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:10.1109/TCYB.2017.2747400)

feature selection, unsupervised learning, diversity

Liu, Yanbei, Liu, Kaihua, Zhang, Changqing, Wang, Jing and Wang, Xiao (2016) Unsupervised feature selection via diversity-induced self-representation. Neurocomputing, 219. pp. 350-363. ISSN 0925-2312 (doi:10.1016/j.neucom.2016.09.043)

glaucoma progression prediction, coupled matrix factorization, convolutional neural networks, regularization, regression, multiview learning

Zheng, Yuhui, Xu, Linchuan, Kiwaki, Taichi, Wang, Jing, Murata, Hiroshi, Asaoka, Ryo and Yamanishi, Kenji (2019) Glaucoma progression prediction using retinal thickness via latent space linear regression. In: KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery, pp. 2278-2286. ISBN 978-1450362016 (doi:10.1145/3292500.3330757)

hyperbolic, representation learning, machine learning

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, Nitanda, Atsushi, Wang, Jing, Xu, Linchuan, Yamanishi, Kenji and Cavazza, Marc (2021) Generalization Error Bound for Hyperbolic Ordinal Embedding. In: Proceedings of the 38th International Conference on Machine Learning. Volume 139: International Conference on Machine Learning, 18th - 24th July 2021, Virtual. Proceedings of Machine Learning Research (PMLR) Press - Journal of Machine Learning Research (JMLR), Cambridge MA, USA, pp. 10011-10021. ISSN 1938-7228 (Print), 2640-3498 (Online)

Suzuki, Atsushi, Suzuki, Nitanda, Wang, Jing, Xu, Linchuan, Yamanishi, Kenji and Cavazza, Marc (2021) Generalization error bounds for graph embedding using negative sampling: linear vs hyperbolic. In: Advances in Neural Information Processing Systems (NeurIPS 2021). Curran Associates Inc. - Neural Information Processing Systems Foundation Inc. (NeurIPS) - ACM, New York, US, 1243 -1255. ISBN 978-1713845393

low rank representation, subspace clustering, semi-supervised learning

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:10.1109/TCYB.2016.2618852)

machine learning; classification machine learning; multi-instance; multi-label; multi-view learning data mining; classification, semi-supervised learning

Huang, Jun, Xu, Linchuan, Wang, Jing, Feng, Lei and Yamanishi, Kenji (2020) Discovering latent class labels for multi-label learning. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization (IJCAI), pp. 3058-3064. ISBN 978-0999241165 (doi:10.24963/ijcai.2020/423)

multi-component, NMF, clustering

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:10.24963/ijcai.2017/407)

multi-view learning, concept factorization

Zhan, Kun, Shi, Jinhui, Wang, Jing, Wang, Haibo and Xie, Yuange (2018) Adaptive structure concept factorization for multiview clustering. Neural Computation, 30 (4). pp. 1080-1103. ISSN 0899-7667 (Print), 1530-888X (Online) (doi:10.1162/NECO_a_01055)

multi-view learning, concept factorization, document clustering, manifold learning

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:10.1016/j.jvcir.2017.02.019)

multi-view learning, semi-supervised learning, nonnegative matrix factorization

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:10.1007/978-3-319-46672-9_49)

network embedding, heterogeneous information networks, multi-label classification, link prediction

Xu, Linchuan, Wang, Jing, He, Lifang, Cao, Jiannong, Wei, Xiaokai, Yu, Phillip S. and Yamanishi, Kenji (2019) MixSp: a framework for embedding heterogeneous information networks with arbitrary number of node and edge types. IEEE Transactions on Knowledge and Data Engineering, 33 (6). pp. 2627-2639. ISSN 1041-4347 (Print), 1558-2191 (Online) (doi:10.1109/TKDE.2019.2955945)

network embedding, nonnegative matrix factorization, unsupervised learnng

Wang, Xiao, Cui, Peng, Wang, Jing, Pei, Jian, Zhu, Wenwu and Yang, Shiqiang (2017) Community preserving network embedding. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). AAAI Press, pp. 203-209.

nonnegative matrix factorization, Semi-supervised learning, clustering

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:10.1109/IJCNN.2015.7280422)

nonnegative matrix factorization, ordered structure, unsupervised learning

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:10.1109/IJCNN.2017.7965892)

nonnegative matrix factorization, ranking preserving, semi-supervised learning

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:10.24963/ijcai.2018/385)

subspace clustering, attribute learning, unsupervised learning

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:10.24963/ijcai.2019/516)

subspace clustering, unsupervised learning, orderly embedding

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:10.1609/aaai.v33i01.33015264)

trust prediction, low-rank representation

Wang, Xiao, Zhang, Ziwei, Wang, Jing, Cui, Peng and Yang, Shiqiang (2018) Power-law distribution aware trust prediction. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence, pp. 3564-3570. ISBN 978-0999241127 (doi:10.24963/ijcai.2018/495)

unsupervised learning, multiview clustering, image retrieval, graph learning

Zhan, Kun, Nie, Feiping, Wang, Jing and Yang, Yi (2018) Multiview consensus graph clustering. IEEE Transactions on Image Processing, 28 (3). pp. 1261-1270. ISSN 1057-7149 (Print), 1941-0042 (Online) (doi:10.1109/TIP.2018.2877335)