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Noise-Resistant Commonality and Individuality Label Learning for Multiview Multilabel Feature Selection Using Fuzzy
IEEE Transactions on Cybernetics
|November 11, 2025
Summary
This study introduces a novel multiview multilabel feature selection method. It effectively integrates consensus and complementary information while mitigating noise for improved accuracy.
Area of Science:
- Machine Learning
- Data Mining
- Artificial Intelligence
Background:
- Multiview multilabel feature selection is crucial for handling large, complex datasets.
- Existing methods often fragment consensus and complementary information, leading to noise and ambiguity.
- Current approaches neglect view-label correlations, impacting view weight accuracy.
Purpose of the Study:
- To propose an integrated multiview multilabel feature selection method.
- To address noise and improve the accuracy of view weight estimation.
- To jointly learn commonality and individuality label structures.
Main Methods:
- Nonnegative matrix factorization for commonality label matrix.
- Fuzzy mutual information for view weights.
- Noise label matrices for noise mitigation.
- Sparse model-based optimization with convergence proof.
Main Results:
- Demonstrated effectiveness across multiple benchmark datasets.
- Improved handling of noise and ambiguity in feature selection.
- Accurate estimation of view weights by considering view-label correlations.
Conclusions:
- The proposed method effectively integrates consensus and complementary information.
- Noise resistance and accurate view weighting enhance feature selection performance.
- The approach offers a robust solution for multiview multilabel feature selection problems.
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