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Interpretable Multi-View Clustering
May 7, 2024, 4:42 a.m. | Mudi Jiang, Lianyu Hu, Zengyou He, Zhikui Chen
cs.LG updates on arXiv.org arxiv.org
Abstract: Multi-view clustering has become a significant area of research, with numerous methods proposed over the past decades to enhance clustering accuracy. However, in many real-world applications, it is crucial to demonstrate a clear decision-making process-specifically, explaining why samples are assigned to particular clusters. Consequently, there remains a notable gap in developing interpretable methods for clustering multi-view data. To fill this crucial gap, we make the first attempt towards this direction by introducing an interpretable multi-view …
abstract accuracy applications arxiv become clear clustering cs.lg decision gap however making process research samples type view world
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