Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering

Jie Wen, Shijie Deng, Waikeung Wong, Guoqing Chao, Chao Huang, Lunke Fei, Yong Xu
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:52762-52778, 2024.

Abstract

As a branch of clustering, multi-view clustering has received much attention in recent years. In practical applications, a common phenomenon is that partial views of some samples may be missing in the collected multi-view data, which poses a severe challenge to design the multi-view learning model and explore complementary and consistent information. Currently, most of the incomplete multi-view clustering methods only focus on exploring the information of available views while few works study the missing view recovery for incomplete multi-view learning. To this end, we propose an innovative diffusion-based missing view generation (DMVG) network. Moreover, for the scenarios with high missing rates, we further propose an incomplete multi-view data augmentation strategy to enhance the recovery quality for the missing views. Extensive experimental results show that the proposed DMVG can not only accurately predict missing views, but also further enhance the subsequent clustering performance in comparison with several state-of-the-art incomplete multi-view clustering methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-wen24c, title = {Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering}, author = {Wen, Jie and Deng, Shijie and Wong, Waikeung and Chao, Guoqing and Huang, Chao and Fei, Lunke and Xu, Yong}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {52762--52778}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/wen24c/wen24c.pdf}, url = {https://proceedings.mlr.press/v235/wen24c.html}, abstract = {As a branch of clustering, multi-view clustering has received much attention in recent years. In practical applications, a common phenomenon is that partial views of some samples may be missing in the collected multi-view data, which poses a severe challenge to design the multi-view learning model and explore complementary and consistent information. Currently, most of the incomplete multi-view clustering methods only focus on exploring the information of available views while few works study the missing view recovery for incomplete multi-view learning. To this end, we propose an innovative diffusion-based missing view generation (DMVG) network. Moreover, for the scenarios with high missing rates, we further propose an incomplete multi-view data augmentation strategy to enhance the recovery quality for the missing views. Extensive experimental results show that the proposed DMVG can not only accurately predict missing views, but also further enhance the subsequent clustering performance in comparison with several state-of-the-art incomplete multi-view clustering methods.} }
Endnote
%0 Conference Paper %T Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering %A Jie Wen %A Shijie Deng %A Waikeung Wong %A Guoqing Chao %A Chao Huang %A Lunke Fei %A Yong Xu %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-wen24c %I PMLR %P 52762--52778 %U https://proceedings.mlr.press/v235/wen24c.html %V 235 %X As a branch of clustering, multi-view clustering has received much attention in recent years. In practical applications, a common phenomenon is that partial views of some samples may be missing in the collected multi-view data, which poses a severe challenge to design the multi-view learning model and explore complementary and consistent information. Currently, most of the incomplete multi-view clustering methods only focus on exploring the information of available views while few works study the missing view recovery for incomplete multi-view learning. To this end, we propose an innovative diffusion-based missing view generation (DMVG) network. Moreover, for the scenarios with high missing rates, we further propose an incomplete multi-view data augmentation strategy to enhance the recovery quality for the missing views. Extensive experimental results show that the proposed DMVG can not only accurately predict missing views, but also further enhance the subsequent clustering performance in comparison with several state-of-the-art incomplete multi-view clustering methods.
APA
Wen, J., Deng, S., Wong, W., Chao, G., Huang, C., Fei, L. & Xu, Y.. (2024). Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:52762-52778 Available from https://proceedings.mlr.press/v235/wen24c.html.

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