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Noise-insensitive discriminative subspace fuzzy clustering
Journal of Applied Statistics ( IF 1.5 ) Pub Date : 2021-06-16 , DOI: 10.1080/02664763.2021.1937583
Xiaobin Zhi 1 , Tongjun Yu 2 , Longtao Bi 2 , Yalan Li 2
Affiliation  

Discriminative subspace clustering (DSC) can make full use of linear discriminant analysis (LDA) to reduce the dimension of data and achieve effective clustering high-dimension data by clustering low-dimension data in discriminant subspace. However, most existing DSC algorithms do not consider the noise and outliers that may be contained in data sets, and when they are applied to the data sets with noise or outliers, and they often obtain poor performance due to the influence of noise and outliers. In this paper, we address the problem of the sensitivity of DSC to noise and outlier. Replacing the Euclidean distance in the objective function of LDA by an exponential non-Euclidean distance, we first develop a noise-insensitive LDA (NILDA) algorithm. Then, combining the proposed NILDA and a noise-insensitive fuzzy clustering algorithm: AFKM, we propose a noise-insensitive discriminative subspace fuzzy clustering (NIDSFC) algorithm. Experiments on some benchmark data sets show the effectiveness of the proposed NIDSFC algorithm.



中文翻译:

噪声不敏感判别子空间模糊聚类

判别子空间聚类(DSC)可以充分利用线性判别分析(LDA)对数据进行降维,通过在判别子空间对低维数据进行聚类,实现对高维数据的有效聚类。然而,现有的大多数DSC算法都没有考虑数据集中可能包含的噪声和异常值,当它们应用于含有噪声或异常值的数据集时,往往会因为噪声和异常值的影响而获得较差的性能。在本文中,我们解决了 DSC 对噪声和异常值的敏感性问题。用指数非欧几里德距离代替 LDA 目标函数中的欧几里德距离,我们首先开发了一种噪声不敏感的 LDA (NILDA) 算法。然后,结合所提出的 NILDA 和噪声不敏感的模糊聚类算法:AFKM,我们提出了一种噪声不敏感的判别子空间模糊聚类(NIDSFC)算法。在一些基准数据集上的实验表明了所提出的 NIDSFC 算法的有效性。

更新日期:2021-06-16
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