The success of fuzzy clustering heavily relies on the features of the input data. Based on the fact that deep architectures are able to more accurately characterize the data representations in a layer-by-layer manner, this paper proposes a novel feature mapping technique called cascaded hidden-space (CHS) feature mapping and investigates its combination with classical fuzzy c-means (FCM) and fuzzy c-regressions (FCR). Since the parameters between the layers of CHS feature mapping are randomly generated and need not be tuned layer-by-layer, CHS is easily implemented with less training data. By performing classical FCM in CHS, a novel fuzzy clustering framework called CHS-FCM is developed; several of its variants are presented using different dimension-reduction methods in a CHS-FCM clustering framework. The combination of CHS-FCM with nonlinear switch regressions is called CHS-FCR, and it performs FCR in CHS. The proposed CHS-FCR provides better results than FCR for nonlinear process modeling. Both CHS-FCM and CHS-FCR exhibit low memory consumption and require less training data. The experimental results verify the superiority of the proposed methods over classical fuzzy clustering methods.

中文翻译：

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大型数据集上的级联隐藏空间特征映射、模糊聚类和非线性切换回归


模糊聚类的成功在很大程度上依赖于输入数据的特征。基于深层架构能够更准确地以逐层方式表征数据表示的事实，本文提出了一种称为级联隐藏空间（CHS）特征映射的新颖特征映射技术，并研究了其与经典模糊的结合c 均值 (FCM) 和模糊 c 回归 (FCR)。由于CHS特征映射各层之间的参数是随机生成的，不需要逐层调整，因此CHS可以用较少的训练数据轻松实现。通过在CHS中执行经典的FCM，开发了一种称为CHS-FCM的新型模糊聚类框架；它的几个变体是在 CHS-FCM 聚类框架中使用不同的降维方法提出的。 CHS-FCM与非线性开关回归的组合称为CHS-FCR，它在CHS中执行FCR。对于非线性过程建模，所提出的 CHS-FCR 提供了比 FCR 更好的结果。 CHS-FCM 和 CHS-FCR 均表现出较低的内存消耗，并且需要较少的训练数据。实验结果验证了该方法相对于经典模糊聚类方法的优越性。