Papers › COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction

COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction

22 Mar 2021CVPR 2021 1archive 2025-07-28

Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, Xi Peng

In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data. To this end, we propose a novel objective that incorporates representation learning and data recovery into a unified framework from the view of information theory. To be specific, the informative and consistent representation is learned by maximizing the mutual information across different views through contrastive learning, and the missing views are recovered by minimizing the conditional entropy of different views through dual prediction. To the best of our knowledge, this could be the first work to provide a theoretical framework that unifies the consistent representation learning and cross-view data recovery. Extensive experimental results show the proposed method remarkably outperforms 10 competitive multi-view clustering methods on four challenging datasets. The code is available at https://pengxi.me.

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Tasks

ClusteringContrastive LearningIncomplete multi-view clusteringMULTI-VIEW LEARNINGPredictionRepresentation Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Incomplete multi-view clustering n-MNIST COMPLETER NMI 75.23 #1 of 1 Archive leaderboard report

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