Using unsupervised learning methods for clinical diagnosis is very meaningful. In this study, we propose an unsupervised multi-view & multi-medoid variant-entropy-based fuzzy clustering (M 2 VEFC) method for epilepsy EEG signals detecting. Comparing with existing related studies, M 2 VEFC has four main merits and contributions: (1) Features in original EEG data are represented from different perspectives that can provide more pattern information for epilepsy signals detecting. (2) During multi-view modeling, multi-medoids are used to capture the structure of clusters in each view. Furthermore, we assume that the medoids in a cluster observed from different views should keep invariant, which is taken as one of the collaborative learning mechanisms in this study. (3) A variant entropy is designed as another collaborative learning mechanism in which view weight learning is controlled by a user-free parameter. The parameter is derived from the distribution of samples in each view such that the learned weights have more discrimination. (4) M 2 VEFC does not need original data as its input—it only needs a similarity matrix and feature statistical information. Therefore, the original data are not exposed to users and hence the privacy is protected. We use several different kinds of feature extraction techniques to extract several groups of features as multi-view data from original EEG data to test the proposed method M 2 VEFC. Experimental results indicate M 2 VEFC achieves a promising performance that is better than benchmarking models.

中文翻译：

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使用具有隐私保护的基于多视图和多中心熵的聚类进行癫痫诊断

使用无监督学习方法进行临床诊断是非常有意义的。在这项研究中，我们提出了一种基于无监督多视图和多中心变体熵的模糊聚类（M2VEFC) 癫痫脑电信号检测方法。与现有的相关研究相比，M2VEFC有四个主要优点和贡献：（1）原始EEG数据中的特征从不同的角度表示，可以为癫痫信号检测提供更多的模式信息。(2) 在多视图建模过程中，使用多中心点来捕捉每个视图中簇的结构。此外，我们假设从不同视图观察到的集群中的中心点应该保持不变，这被视为本研究中的协作学习机制之一。(3) 变体熵被设计为另一种协作学习机制，其中视图权重学习由用户自由参数控制。该参数是从每个视图中的样本分布推导出来的，这样学习的权重就具有更多的辨别力。(4) 男2VEFC 不需要原始数据作为输入——它只需要一个相似度矩阵和特征统计信息。因此，原始数据不会暴露给用户，从而保护隐私。我们使用几种不同的特征提取技术从原始 EEG 数据中提取多组特征作为多视图数据，以测试提出的方法 M2VEFC。实验结果表明 M2VEFC 实现了比基准模型更好的有希望的性能。