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May, 2024
解释性多视角聚类
Interpretable Multi-View Clustering
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Mudi Jiang, Lianyu Hu, Zengyou He, Zhikui Chen
TL;DR
多视角聚类是一个重要的研究领域,本研究提出了一个可解释的多视角聚类框架,通过提取每个视角的嵌入特征和生成伪标签来引导决策树的初始构建,并在优化特征表示以及改进解释性决策树的同时,为多视角数据提供一个透明的聚类过程,实验结果表明,该方法在聚类性能上与最先进的多视角聚类方法可媲美。
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
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