Automatic classification methods widely used for diagnosing and analyzing COVID-19 cases. These methods assume known labels and rely on a single view of the dataset. Given the prevalence of COVID-19 cases and the extensive volume of patient records lacking labels, this communication underscores our unique approach—conducting the first study on COVID-19 case diagnosis in an unsupervised manner. Our work operates under the assumption of prior knowledge regarding the number of classes, such as COVID-19, pneumonia, and normal, in a case study.

中文翻译：

---

针对 COVID-19 的无监督射线照片聚类：基于图的多视图聚类的使用

自动分类方法广泛用于诊断和分析 COVID-19 病例。这些方法假设已知标签并依赖于数据集的单一视图。鉴于 COVID-19 病例的流行以及大量缺乏标签的患者记录，本次沟通强调了我们独特的方法——以无人监督的方式开展了首次关于 COVID-19 病例诊断的研究。我们的工作是在案例研究中假设预先了解类别数量（例如 COVID-19、肺炎和正常）的情况下进行的。