Fuzzy objective functions for robust pattern recognition

Tai-Ning Yang, Chih-Jen Lee, Shi-Jim Yen
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引用次数: 8

Abstract

In this paper, we consider the issue of fuzzy objective functions when outliers exist. The outlier set is defined as the complement of the data set. Following this concept, a specially designed fuzzy membership weighted objective function is proposed and the corresponding optimal membership is derived. Based on the proposed robust objective functions, algorithms for clustering are implemented. Artificially generated data are used for comparison.
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鲁棒模式识别的模糊目标函数
本文考虑了存在异常值时模糊目标函数的问题。离群集被定义为数据集的补集。在此基础上,提出了一种特殊设计的模糊隶属度加权目标函数,并推导出相应的最优隶属度。基于所提出的鲁棒目标函数,实现了聚类算法。人工生成的数据用于比较。
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