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On robust fuzzy c-regression models

机译：关于鲁棒的模糊c回归模型

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摘要

One of the most popular clustering methods based on minimization of a criterion function is the fuzzy c-means one. Its generalization by application of hyperplane shaped prototypes of the clusters is known as the Fuzzy C-Regression Models (FCRM) method. Although with this generalization many new applications of clustering emerged, it appeared to be rather sensitive to poor initialization and to the presence of noise and outliers in data. In this paper we introduce a new objective function, using the Huber's M-estimators and the Yager's OWA operators to overcome the disadvantages of the approach considered. We derive and describe an algorithm for minimization of the objective function defined. We have called it the Fuzzy C-Ordered-Regression Models (FCORM) clustering algorithm. The algorithm is compared to a few other important reference ones. To this end experiments on synthetic data with various types of noise and different numbers of outliers are carried out. We investigate the methods performance in the conditions that can be encountered in signal analysis. Large-scale simulations demonstrate the competitiveness and usefulness of the method proposed. (C) 2014 Elsevier B.V. All rights reserved.
机译：基于准则函数最小化的最流行的聚类方法之一是模糊c均值。通过应用集群的超平面形状原型对其进行的概括称为模糊C回归模型（FCRM）方法。尽管有了这种概括，但是出现了许多新的群集应用程序，但它似乎对不良的初始化以及数据中存在噪声和异常值非常敏感。在本文中，我们介绍了一种新的目标函数，它使用Huber的M估计量和Yager的OWA运算符来克服所考虑方法的缺点。我们导出并描述了用于最小化所定义目标函数的算法。我们称其为模糊C阶回归模型（FCORM）聚类算法。该算法与其他一些重要的参考算法进行了比较。为此，对具有各种类型的噪声和不同数量的异常值的合成数据进行了实验。我们在信号分析可能遇到的条件下研究方法的性能。大规模仿真证明了所提出方法的竞争力和实用性。 （C）2014 Elsevier B.V.保留所有权利。

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