Incomplete Multi-View Clustering via Neighborhood-Conditioned Diffusion

Qian Guo, Gaohui Zuo, Bingbing Jiang, Guangrui Fan, Zhihua Cui, Xinyan Liang, Jianjian Ding
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:38794-38808, 2026.

Abstract

Incomplete multi-view clustering (IMVC) aims to uncover shared clustering structures from heterogeneous views with partial observations. Recently, existing generative IMVC methods have made significant progress in this field; however, they still remain limited in two aspects. On the one hand, they rely on weak cross-view signals, resulting in unstable latent recovery when facing missing data. On the other hand, they overlook stable cross-view neighborhood structures, leading to weak structural constraint. To address these limitations, we propose neighborhood-conditioned diffusion for incomplete multi-view clustering (IMVC-NCD), which achieves robust latent completion. Our method learns compact view-specific latent representations and constructs a unified conditioning vector by aggregating stable local neighborhood structures from available views while encoding missingness states, providing reliable guidance for diffusion-based denoising. With neighborhood-level conditioning, IMVC-NCD produces semantically aligned and view-consistent latent representations that are well suited for clustering, even under high missing-view ratios. Extensive experiments on four benchmark datasets demonstrate the effectiveness and robustness of our method compared with state-of-the-art IMVC approaches. Our code is available at https://github.com/zgh1115/IMVC-NCD.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-guo26an, title = {Incomplete Multi-View Clustering via Neighborhood-Conditioned Diffusion}, author = {Guo, Qian and Zuo, Gaohui and Jiang, Bingbing and Fan, Guangrui and Cui, Zhihua and Liang, Xinyan and Ding, Jianjian}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {38794--38808}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/guo26an/guo26an.pdf}, url = {https://proceedings.mlr.press/v306/guo26an.html}, abstract = {Incomplete multi-view clustering (IMVC) aims to uncover shared clustering structures from heterogeneous views with partial observations. Recently, existing generative IMVC methods have made significant progress in this field; however, they still remain limited in two aspects. On the one hand, they rely on weak cross-view signals, resulting in unstable latent recovery when facing missing data. On the other hand, they overlook stable cross-view neighborhood structures, leading to weak structural constraint. To address these limitations, we propose neighborhood-conditioned diffusion for incomplete multi-view clustering (IMVC-NCD), which achieves robust latent completion. Our method learns compact view-specific latent representations and constructs a unified conditioning vector by aggregating stable local neighborhood structures from available views while encoding missingness states, providing reliable guidance for diffusion-based denoising. With neighborhood-level conditioning, IMVC-NCD produces semantically aligned and view-consistent latent representations that are well suited for clustering, even under high missing-view ratios. Extensive experiments on four benchmark datasets demonstrate the effectiveness and robustness of our method compared with state-of-the-art IMVC approaches. Our code is available at https://github.com/zgh1115/IMVC-NCD.} }
Endnote
%0 Conference Paper %T Incomplete Multi-View Clustering via Neighborhood-Conditioned Diffusion %A Qian Guo %A Gaohui Zuo %A Bingbing Jiang %A Guangrui Fan %A Zhihua Cui %A Xinyan Liang %A Jianjian Ding %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-guo26an %I PMLR %P 38794--38808 %U https://proceedings.mlr.press/v306/guo26an.html %V 306 %X Incomplete multi-view clustering (IMVC) aims to uncover shared clustering structures from heterogeneous views with partial observations. Recently, existing generative IMVC methods have made significant progress in this field; however, they still remain limited in two aspects. On the one hand, they rely on weak cross-view signals, resulting in unstable latent recovery when facing missing data. On the other hand, they overlook stable cross-view neighborhood structures, leading to weak structural constraint. To address these limitations, we propose neighborhood-conditioned diffusion for incomplete multi-view clustering (IMVC-NCD), which achieves robust latent completion. Our method learns compact view-specific latent representations and constructs a unified conditioning vector by aggregating stable local neighborhood structures from available views while encoding missingness states, providing reliable guidance for diffusion-based denoising. With neighborhood-level conditioning, IMVC-NCD produces semantically aligned and view-consistent latent representations that are well suited for clustering, even under high missing-view ratios. Extensive experiments on four benchmark datasets demonstrate the effectiveness and robustness of our method compared with state-of-the-art IMVC approaches. Our code is available at https://github.com/zgh1115/IMVC-NCD.
APA
Guo, Q., Zuo, G., Jiang, B., Fan, G., Cui, Z., Liang, X. & Ding, J.. (2026). Incomplete Multi-View Clustering via Neighborhood-Conditioned Diffusion. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:38794-38808 Available from https://proceedings.mlr.press/v306/guo26an.html.

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