Collaborative Multi-view Learning from Crowds

Shanshan Si, Liangxiao Jiang, Wenjun Zhang, Chaoqun Li, Liangjun Yu
Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, PMLR 337:6310-6320, 2026.

Abstract

As an effective post-processing approach to improving the quality of integrated labels, noise correction has received extensive attention in recent years. Recent studies have shown that utilizing information from both the original attribute view and the multiple noisy label view can significantly improve the performance of noise correction. However, most existing algorithms often learn information independently on each view, making them difficult to fully exploit the complementary information between these two views. To address this limitation, we propose a noise correction algorithm called collaborative multi-view learning from crowds (CMLFC). CMLFC first constructs instance correlation graphs on the original attribute view and the multiple noisy label view, respectively, and designs dedicated encoders to obtain view-specific representations. To capture the deep complementary information between these two views, it designs a contrastive fusion encoder to distill common representations from these two view-specific representations, and then forms a common view. Next, it trains three view-specific classifiers on these three views, and designs a joint loss function to collaboratively train these encoders and classifiers, enabling information to flow bidirectionally among views, thereby improving the quality of representations in each view. Finally, it employs the trained view-specific encoders and classifiers to obtain each instance’s representations and class probability distributions, respectively, which are then used to update its integrated label. Extensive experiments on real-world crowdsourced datasets validate the effectiveness, ablation, and sensitivity of CMLFC. Our codes and datasets are available at https://github.com/jiangliangxiao/CMLFC.

Cite this Paper

BibTeX
@InProceedings{pmlr-v337-si26a, title = {Collaborative Multi-view Learning from Crowds}, author = {Si, Shanshan and Jiang, Liangxiao and Zhang, Wenjun and Li, Chaoqun and Yu, Liangjun}, booktitle = {Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence}, pages = {6310--6320}, year = {2026}, editor = {Perković, Emilija and Malinsky, Daniel}, volume = {337}, series = {Proceedings of Machine Learning Research}, month = {17--21 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v337/main/assets/si26a/si26a.pdf}, url = {https://proceedings.mlr.press/v337/si26a.html}, abstract = {As an effective post-processing approach to improving the quality of integrated labels, noise correction has received extensive attention in recent years. Recent studies have shown that utilizing information from both the original attribute view and the multiple noisy label view can significantly improve the performance of noise correction. However, most existing algorithms often learn information independently on each view, making them difficult to fully exploit the complementary information between these two views. To address this limitation, we propose a noise correction algorithm called collaborative multi-view learning from crowds (CMLFC). CMLFC first constructs instance correlation graphs on the original attribute view and the multiple noisy label view, respectively, and designs dedicated encoders to obtain view-specific representations. To capture the deep complementary information between these two views, it designs a contrastive fusion encoder to distill common representations from these two view-specific representations, and then forms a common view. Next, it trains three view-specific classifiers on these three views, and designs a joint loss function to collaboratively train these encoders and classifiers, enabling information to flow bidirectionally among views, thereby improving the quality of representations in each view. Finally, it employs the trained view-specific encoders and classifiers to obtain each instance’s representations and class probability distributions, respectively, which are then used to update its integrated label. Extensive experiments on real-world crowdsourced datasets validate the effectiveness, ablation, and sensitivity of CMLFC. Our codes and datasets are available at https://github.com/jiangliangxiao/CMLFC.} }
Endnote
%0 Conference Paper %T Collaborative Multi-view Learning from Crowds %A Shanshan Si %A Liangxiao Jiang %A Wenjun Zhang %A Chaoqun Li %A Liangjun Yu %B Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Emilija Perković %E Daniel Malinsky %F pmlr-v337-si26a %I PMLR %P 6310--6320 %U https://proceedings.mlr.press/v337/si26a.html %V 337 %X As an effective post-processing approach to improving the quality of integrated labels, noise correction has received extensive attention in recent years. Recent studies have shown that utilizing information from both the original attribute view and the multiple noisy label view can significantly improve the performance of noise correction. However, most existing algorithms often learn information independently on each view, making them difficult to fully exploit the complementary information between these two views. To address this limitation, we propose a noise correction algorithm called collaborative multi-view learning from crowds (CMLFC). CMLFC first constructs instance correlation graphs on the original attribute view and the multiple noisy label view, respectively, and designs dedicated encoders to obtain view-specific representations. To capture the deep complementary information between these two views, it designs a contrastive fusion encoder to distill common representations from these two view-specific representations, and then forms a common view. Next, it trains three view-specific classifiers on these three views, and designs a joint loss function to collaboratively train these encoders and classifiers, enabling information to flow bidirectionally among views, thereby improving the quality of representations in each view. Finally, it employs the trained view-specific encoders and classifiers to obtain each instance’s representations and class probability distributions, respectively, which are then used to update its integrated label. Extensive experiments on real-world crowdsourced datasets validate the effectiveness, ablation, and sensitivity of CMLFC. Our codes and datasets are available at https://github.com/jiangliangxiao/CMLFC.
APA
Si, S., Jiang, L., Zhang, W., Li, C. & Yu, L.. (2026). Collaborative Multi-view Learning from Crowds. Proceedings of the 42nd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 337:6310-6320 Available from https://proceedings.mlr.press/v337/si26a.html.

Related Material