Items where Author is "Yamanishi, Kenji"

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Number of items: 40.

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

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)

convolutive nonnegative matrix factorization

Suzuki, Atsushi, Miyaguchi, Kohei and Yamanishi, Kenji (2017) Structure selection for convolutive non-negative matrix factorization using normalized maximum likelihood coding. In: 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, Barcelona, pp. 1221-1226. ISBN 978-1509054749 ISSN 2374-8486 (doi:https://doi.org/10.1109/ICDM.2016.0163)

coupled matrix factorization

glaucoma progression prediction

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.

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

link prediction

low-dimensionality

machine learning

multi-instance

multi-label

multi-label classification

multi-view learning data mining

multiview learning

network embedding

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)

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)