Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm

Huayi Tang, Yong Liu
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:21090-21110, 2022.

Abstract

Incomplete multi-view clustering is a significant but challenging task. Although jointly imputing incomplete samples and conducting clustering has been shown to achieve promising performance, learning from both complete and incomplete data may be worse than learning only from complete data, particularly when imputed views are semantic inconsistent with missing views. To address this issue, we propose a novel framework to reduce the clustering performance degradation risk from semantic inconsistent imputed views. Concretely, by the proposed bi-level optimization framework, missing views are dynamically imputed from the learned semantic neighbors, and imputed samples are automatically selected for training. In theory, the empirical risk of the model is no higher than learning only from complete data, and the model is never worse than learning only from complete data in terms of expected risk with high probability. Comprehensive experiments demonstrate that the proposed method achieves superior performance and efficient safe incomplete multi-view clustering.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-tang22c, title = {Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm}, author = {Tang, Huayi and Liu, Yong}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {21090--21110}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/tang22c/tang22c.pdf}, url = {https://proceedings.mlr.press/v162/tang22c.html}, abstract = {Incomplete multi-view clustering is a significant but challenging task. Although jointly imputing incomplete samples and conducting clustering has been shown to achieve promising performance, learning from both complete and incomplete data may be worse than learning only from complete data, particularly when imputed views are semantic inconsistent with missing views. To address this issue, we propose a novel framework to reduce the clustering performance degradation risk from semantic inconsistent imputed views. Concretely, by the proposed bi-level optimization framework, missing views are dynamically imputed from the learned semantic neighbors, and imputed samples are automatically selected for training. In theory, the empirical risk of the model is no higher than learning only from complete data, and the model is never worse than learning only from complete data in terms of expected risk with high probability. Comprehensive experiments demonstrate that the proposed method achieves superior performance and efficient safe incomplete multi-view clustering.} }
Endnote
%0 Conference Paper %T Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm %A Huayi Tang %A Yong Liu %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-tang22c %I PMLR %P 21090--21110 %U https://proceedings.mlr.press/v162/tang22c.html %V 162 %X Incomplete multi-view clustering is a significant but challenging task. Although jointly imputing incomplete samples and conducting clustering has been shown to achieve promising performance, learning from both complete and incomplete data may be worse than learning only from complete data, particularly when imputed views are semantic inconsistent with missing views. To address this issue, we propose a novel framework to reduce the clustering performance degradation risk from semantic inconsistent imputed views. Concretely, by the proposed bi-level optimization framework, missing views are dynamically imputed from the learned semantic neighbors, and imputed samples are automatically selected for training. In theory, the empirical risk of the model is no higher than learning only from complete data, and the model is never worse than learning only from complete data in terms of expected risk with high probability. Comprehensive experiments demonstrate that the proposed method achieves superior performance and efficient safe incomplete multi-view clustering.
APA
Tang, H. & Liu, Y.. (2022). Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:21090-21110 Available from https://proceedings.mlr.press/v162/tang22c.html.

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