LE

BaseLE

class pyldl.algorithms.base.BaseLE(random_state: int | None = None)

LIBLE

class pyldl.algorithms.LIBLE(*args, **kwargs)

LIBLE is proposed in paper [LE-ZZT23].

LEVI

class pyldl.algorithms.LEVI(*args, **kwargs)

LEVI is proposed in paper [LE-XSLG20].

See also:

[LEVI-XSZ+23]

Ning Xu, Jun Shu, Renyi Zheng, Xin Geng, Deyu Meng, and Min-Ling Zhang. Variational label enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):6537–6551, 2023. URL: https://doi.org/10.1109/TPAMI.2022.3203678.

GLLE

class pyldl.algorithms.GLLE(*args, **kwargs)

GLLE is proposed in paper [LE-XTG18].

See also:

[GLLE-XLG21]

Ning Xu, Yun-Peng Liu, and Xin Geng. Label enhancement for label distribution learning. IEEE Transactions on Knowledge and Data Engineering, 33(4):1632–1643, 2021. URL: https://doi.org/10.1109/TKDE.2019.2947040.

ML

class pyldl.algorithms.ML(random_state: int | None = None)

ML is proposed in paper [LE-XTG18].

LP

class pyldl.algorithms.LP(random_state: int | None = None)

LP is proposed in paper [LE-XTG18].

KM

class pyldl.algorithms.KM(random_state: int | None = None)

KM is proposed in paper [LE-XTG18].

FCM

class pyldl.algorithms.FCM(random_state: int | None = None)

FCM is proposed in paper [LE-XTG18].

References

[LE-ZZT23]

Qinghai Zheng, Jihua Zhu, and Haoyu Tang. Label information bottleneck for label enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7497–7506. 2023. URL: https://doi.org/10.1109/CVPR52729.2023.00724.

[LE-XSLG20]

Ning Xu, Jun Shu, Yun-Peng Liu, and Xin Geng. Variational label enhancement. In Proceedings of the International Conference on Machine Learning, 10597–10606. 2020.

[LE-XTG18] (1,2,3,4,5)

Ning Xu, An Tao, and Xin Geng. Label enhancement for label distribution learning. In Proceedings of the International Joint Conference on Artificial Intelligence, 2926–2932. 2018. URL: https://doi.org/10.24963/ijcai.2018/406.

Further Reading

[LE-GWG24]

Yongbiao Gao, Ke Wang, and Xin Geng. Sequential label enhancement. IEEE Transactions on Neural Networks and Learning Systems, 35(5):7204–7215, 2024. URL: https://doi.org/10.1109/TNNLS.2022.3214610.

[LE-JLZ23]

Xiuyi Jia, Yunan Lu, and Fangwen Zhang. Label enhancement by maintaining positive and negative label relation. IEEE Transactions on Knowledge and Data Engineering, 35(2):1708–1720, 2023. URL: https://doi.org/10.1109/TKDE.2021.3093099.

[LE-LLLJ23]

Yunan Lu, Weiwei Li, Huaxiong Li, and Xiuyi Jia. Ranking-preserved generative label enhancement. Machine Learning, 112(12):4693–4721, 2023. URL: https://doi.org/10.1007/s10994-023-06388-9.

[LE-LLJ23]

Yunan Lu, Weiwei Li, and Xiuyi Jia. Label enhancement via joint implicit representation clustering. In Proceedings of the International Joint Conference on Artificial Intelligence, 4019–4027. 2023. URL: https://doi.org/10.24963/ijcai.2023/447.

[LE-LHM+23]

Yunan Lu, Liang He, Fan Min, Weiwei Li, and Xiuyi Jia. Generative label enhancement with gaussian mixture and partial ranking. In Proceedings of the AAAI Conference on Artificial Intelligence, 8975–8983. 2023. URL: https://doi.org/10.1609/aaai.v37i7.26078.

[LE-TCGJ23]

Chao Tan, Sheng Chen, Xin Geng, and Genlin Ji. A novel label enhancement algorithm based on manifold learning. Pattern Recognition, 135:109189, 2023. URL: https://doi.org/10.1016/j.patcog.2022.109189.

[LE-WXLG23]

Ke Wang, Ning Xu, Miaogen Ling, and Xin Geng. Fast label enhancement for label distribution learning. IEEE Transactions on Knowledge and Data Engineering, 35(2):1502–1514, 2023. URL: https://doi.org/10.1109/TKDE.2021.3092406.

[LE-WZZ+23]

Yifei Wang, Yiyang Zhou, Jihua Zhu, Xinyuan Liu, Wenbiao Yan, and Zhiqiang Tian. Contrastive label enhancement. In Proceedings of the International Joint Conference on Artificial Intelligence, 4353–4361. 2023. URL: https://doi.org/10.24963/ijcai.2023/484.

[LE-ZZT+23]

Qinghai Zheng, Jihua Zhu, Haoyu Tang, Xinyuan Liu, Zhongyu Li, and Huimin Lu. Generalized label enhancement with sample correlations. IEEE Transactions on Knowledge and Data Engineering, 35(1):482–495, 2023. URL: https://doi.org/10.1109/TKDE.2021.3073157.

[LE-ZAXG22]

Xingyu Zhao, Yuexuan An, Ning Xu, and Xin Geng. Fusion label enhancement for multi-label learning. In Proceedings of the International Joint Conference on Artificial Intelligence, 3773–3779. 2022. URL: https://doi.org/10.24963/ijcai.2022/524.

[LE-GZG21]

Yongbiao Gao, Yu Zhang, and Xin Geng. Label enhancement for label distribution learning via prior knowledge. In Proceedings of the International Joint Conference on Artificial Intelligence, 3223–3229. 2021. URL: https://doi.org/10.24963/ijcai.2020/446.

[LE-TCJG21]

Chao Tan, Sheng Chen, Genlin Ji, and Xin Geng. A novel probabilistic label enhancement algorithm for multi-label distribution learning. IEEE Transactions on Knowledge and Data Engineering, 34(11):5098–5113, 2021. URL: https://doi.org/10.1109/TKDE.2021.3054465.

[LE-TZZ+20]

Haoyu Tang, Jihua Zhu, Qinghai Zheng, Jun Wang, Shanmin Pang, and Zhongyu Li. Label enhancement with sample correlations via low-rank representation. In Proceedings of the AAAI Conference on Artificial Intelligence, 5932–5939. 2020. URL: https://doi.org/10.1609/aaai.v34i04.6053.

[LE-ZJL20a]

Fangwen Zhang, Xiuyi Jia, and Weiwei Li. Tensor-based multi-view label enhancement for multi-label learning. In Proceedings of the International Joint Conference on Artificial Intelligence, 2369–2375. 2020. URL: https://doi.org/10.24963/ijcai.2020/328.

[LE-ZJL20b]

Wenfang Zhu, Xiuyi Jia, and Weiwei Li. Privileged label enhancement with multi-label learning. In Proceedings of the International Joint Conferences on Artificial Intelligence, 2376–2382. 2020. URL: https://doi.org/10.24963/ijcai.2020/329.

[LE-LXZG19]

Jiaqi Lv, Ning Xu, RenYi Zheng, and Xin Geng. Weakly supervised multi-label learning via label enhancement. In Proceedings of the International Joint Conferences on Artificial Intelligence, 3101–3107. 2019. URL: https://doi.org/10.24963/ijcai.2019/430.

[LE-XLG19]

Ning Xu, Jiaqi Lv, and Xin Geng. Partial label learning via label enhancement. In Proceedings of the AAAI Conference on artificial intelligence, 5557–5564. 2019. URL: https://doi.org/10.1609/aaai.v33i01.33015557.