The content discusses the challenges posed by conflictive instances in multi-view data and introduces the ECML method to address this issue. By providing decision results and reliabilities for conflictive instances, ECML aims to improve accuracy, reliability, and robustness in various tasks such as clustering, retrieval, and recommendation. The theoretical framework, experimental results on real-world datasets, uncertainty estimation, and conflict visualization all contribute to validating the effectiveness of ECML in handling conflictive multi-view data.
The authors highlight the importance of explicitly addressing conflicts between views in multi-view learning systems. They propose a novel approach that not only improves accuracy but also provides insights into uncertainty estimation and conflict quantification. The experimental results demonstrate the superiority of ECML over existing baselines in both normal and conflictive scenarios.
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by Cai Xu,Jiaju... alle arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.16897.pdfDomande più approfondite