将区间2型模糊集集成到深度嵌入聚类中处理不确定性

Kutay Bölat, T. Kumbasar
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引用次数: 1

摘要

处理未标记的数据会带来不确定性的负担,特别是当数据是高维的时候。集群在这方面也不例外,需要特殊处理。在本研究中,我们提出使用区间2型(IT2)模糊集(FSs)和深度学习(DL)方法来处理高维数据聚类过程中出现的不确定性。用区间值参数(IVPs)参数化不同的聚类相似函数来生成it2 - fs。引入这些参数作为聚类分配不确定性的表示。采用深度嵌入聚类(DEC)作为该方法的主干。所得到的IT2模糊聚类推理被集成到DEC中,使得所提出模型的推理和训练在流行的深度学习框架中都是可操作的。因此,对于简单的部署,通过在ivp上引入参数化技巧来重新定义it2 - fs上的约束。比较结果表明,通过IT2-FSs处理不确定性优于基线类型-1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating Interval Type-2 Fuzzy Sets into Deep Embedding Clustering to Cope with Uncertainty
Working with unlabeled data carries the burden of uncertainties especially when the data are high-dimensional. Clustering is not an exception in this aspect and it requires special treatment. In this study, we propose to cope with the uncertainties which occur during clustering high-dimensional data with Interval Type-2 (IT2) Fuzzy Sets (FSs) and Deep Learning (DL) methods. Generation of the IT2-FSs is done with different cluster similarity functions parameterized with Interval Valued Parameters (IVPs). These parameters are introduced as the representations of the uncertainty in cluster assignments. As the backbone of the proposed method, Deep Embedding Clustering (DEC) is employed. The resulting IT2 fuzzy clustering inference is integrated into DEC so that both the inference and the training of the proposed model are operational in popular DL frameworks. Therefore, for a straightforward deployment, the constraints on IT2-FSs are redefined by introducing parameterization tricks upon IVPs. The presented comparative results indicate that coping with the uncertainties through IT2-FSs is superior to their baseline type-1 counterparts.
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