Abstract:
Multiview fuzzy systems use multiview learning to effectively model fuzzy scenarios and obtain an interpretable model. Current multiview fuzzy system studies face several challenges, including how to efficiently collaborate between multiple views with few labeled data. This article investigates transductive multiview fuzzy modeling to solve this problem. Transductive learning reduces the dependency on labeled data by simultaneously learning the fuzzy model and labels using a novel learning criterion. Matrix factorization enhances fuzzy model performance. Collaborative learning between multiple views strengthens the model. The proposed multiview learning method outperforms others, according to experiments.
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