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Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm

ICML 2022 · Learning theory and algorithms › Clustering · approach: Incomplete multi-view clustering

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
adding bi-level weighting of imputed incomplete samples (DSIMVC vs. baselines)DSIMVC · BDGP, MNIST-USPS, CCV, Multi-FashionACCHelped
adding bi-level weighting of imputed samples, robustness at high missing ratioDSIMVC · BDGP, missing ratio 0.5 to 0.7ACC drop: DSIMVC ACC drops 3.2% vs 10.2% for second bestHelped
DSIMVC vs. variant trained only on complete data (ablation)DSIMVC · BDGP, complete samplesclustering performance (NMI)Helped
dynamic semantic-neighbor imputationDSIMVC · MNIST-USPSsemantic consistency ratio of imputationsHelped
hyperparameter variation (k from 3 to 7, gamma from 0.3 to 0.7) (ablation)DSIMVC · NMI sensitivityNMINo clear effect

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