Items where Author is "Wang, Jing"

Group by: Item Type | Uncontrolled Keywords | No Grouping

attribute learning classification classification machine learning clustering community detection concept factorization Constraint matrix convolutional neural networks coupled matrix factorization disability disability policy disability status diversity diversity representation document clustering feature selection glaucoma progression prediction global information graph learning heterogeneous information networks hierarchical structure HRM practices hyperbolic hyperbolic space image retrieval individual variable pay institutional trust link prediction local information low rank representation low-dimensionality low-rank representation Low-rank tensor machine learning manifold learning merit pay multi-component multi-instance multi-label multi-label classification multi-view learning multi-view learning data mining Multi-view representation learning multilevel modelling multiview clustering multiview learning network embedding NMF non-negative matrix factorization (NMF) nonnegative matrix factorization ordered structure orderly embedding Ordinal embedding pay satisfaction ranking preserving regression regularization relational trust representation learning semi-supervised learning Subspace clustering topology information trust in management trust prediction unsupervised learning unsupervised learnng

Number of items: 98.

attribute 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:https://doi.org/10.24963/ijcai.2019/516)

classification

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:https://doi.org/10.24963/ijcai.2020/423)

classification machine learning

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:https://doi.org/10.24963/ijcai.2017/407)

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)

community detection

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:https://doi.org/10.1371/journal.pone.0178029)

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:https://doi.org/10.1016/j.neucom.2017.02.026)

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:https://doi.org/10.1162/NECO_a_01055)

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)

Constraint matrix

Zhang, Changqing ORCID: 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:https://doi.org/10.1007/s11263-020-01307-0)

convolutional neural networks

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:https://doi.org/10.1145/3292500.3330757)

coupled matrix factorization

disability

Shantz, Amanda, Wang, Jing and Malik, Amina (2015) "The Relationships Among Merit Pay, Disability Status, and Trust in Management on Job Satisfaction". In: Academy of Management Conference. (doi:https://doi.org/10.5465/AMBPP.2015.10249abstract)

disability policy

Shantz, Amanda, Wang, Jing and Malik, Amina (2017) Disability status, individual variable pay, and pay satisfaction: does relational and institutional trust make a difference? Human Resource Management, 57 (1). pp. 365-380. ISSN 0090-4848 (Print), 1099-050X (Online) (doi:https://doi.org/10.1002/hrm.21845)

disability status

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:https://doi.org/10.1016/j.neucom.2016.09.043)

diversity representation

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)

document clustering

feature selection

glaucoma progression prediction

global information

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:https://doi.org/10.1109/TIP.2018.2877335)

heterogeneous information networks

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:https://doi.org/10.1109/TKDE.2019.2955945)

hierarchical structure

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.

HRM practices

hyperbolic

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

hyperbolic space

image retrieval

individual variable pay

institutional trust

link prediction

local information

low rank representation

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)

low-dimensionality

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:https://doi.org/10.24963/ijcai.2018/495)

Low-rank tensor

machine learning

manifold learning

merit pay

multi-component

multi-instance

multi-label

multi-label classification

multi-view learning

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)

multi-view learning data mining

Multi-view representation learning

multilevel modelling

multiview clustering

multiview learning

network embedding

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.

NMF

non-negative matrix factorization (NMF)

nonnegative matrix factorization

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, 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)

ordered structure

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:https://doi.org/10.1609/aaai.v33i01.33015264)

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