Variational Label Enhancement

Ning Xu, Jun Shu, Yun-Peng Liu, Xin Geng
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:10597-10606, 2020.

Abstract

Label distribution covers a certain number of labels, representing the degree to which each label describes the instance. When dealing with label ambiguity, label distribution could describe the supervised information in a fine-grained way. Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining label distributions directly. To solve this problem, we consider the label distributions as the latent vectors and infer them from the logical labels in the training datasets by using variational inference. After that, we induce a predictive model to train the label distribution data by employing the multi-output regression technique. The recovery experiment on thirteen real-world LDL datasets and the predictive experiment on ten multi-label learning datasets validate the advantage of our approach over the state-of-the-art approaches.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-xu20g, title = {Variational Label Enhancement}, author = {Xu, Ning and Shu, Jun and Liu, Yun-Peng and Geng, Xin}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {10597--10606}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/xu20g/xu20g.pdf}, url = {https://proceedings.mlr.press/v119/xu20g.html}, abstract = {Label distribution covers a certain number of labels, representing the degree to which each label describes the instance. When dealing with label ambiguity, label distribution could describe the supervised information in a fine-grained way. Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining label distributions directly. To solve this problem, we consider the label distributions as the latent vectors and infer them from the logical labels in the training datasets by using variational inference. After that, we induce a predictive model to train the label distribution data by employing the multi-output regression technique. The recovery experiment on thirteen real-world LDL datasets and the predictive experiment on ten multi-label learning datasets validate the advantage of our approach over the state-of-the-art approaches.} }
Endnote
%0 Conference Paper %T Variational Label Enhancement %A Ning Xu %A Jun Shu %A Yun-Peng Liu %A Xin Geng %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-xu20g %I PMLR %P 10597--10606 %U https://proceedings.mlr.press/v119/xu20g.html %V 119 %X Label distribution covers a certain number of labels, representing the degree to which each label describes the instance. When dealing with label ambiguity, label distribution could describe the supervised information in a fine-grained way. Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining label distributions directly. To solve this problem, we consider the label distributions as the latent vectors and infer them from the logical labels in the training datasets by using variational inference. After that, we induce a predictive model to train the label distribution data by employing the multi-output regression technique. The recovery experiment on thirteen real-world LDL datasets and the predictive experiment on ten multi-label learning datasets validate the advantage of our approach over the state-of-the-art approaches.
APA
Xu, N., Shu, J., Liu, Y. & Geng, X.. (2020). Variational Label Enhancement. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:10597-10606 Available from https://proceedings.mlr.press/v119/xu20g.html.

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