This paper introduces the popular sparse representation method into the classical fuzzy c-means clustering algorithm, and presents a novel fuzzy clustering algorithm, called fuzzy double c-means based on sparse self-representation (FDCM_SSR). The major characteristic of FDCM_SSR is that it can simultaneously address two datasets with different dimensions, and has two kinds of corresponding cluster centers. The first one is the basic feature set that represents the basic physical property of each sample itself. The second one is learned from the basic feature set by solving a spare self-representation model, referred to as discriminant feature set, which reflects the global structure of the sample set. The spare self-representation model employs dataset itself as dictionary of sparse representation. It has good category distinguishing ability, noise robustness, and data-adaptiveness, which enhance the clustering and generalization performance of FDCM_SSR. Experiments on different datasets and images show that FDCM_SSR is more competitive than other state-of-the-art fuzzy clustering algorithms.

中文翻译：

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基于稀疏自我表示的模糊双C均值聚类

本文将流行的稀疏表示方法引入到经典的模糊c-means聚类算法中，提出了一种新颖的模糊聚类算法，称为基于稀疏自我表示的模糊双c-均值(FDCM_SSR)。FDCM_SSR 的主要特点是可以同时处理两个不同维度的数据集，并且有两种对应的聚类中心。第一个是基本特征集，代表每个样本本身的基本物理属性。第二个是通过求解一个备用的自我表示模型从基本特征集中学习的，称为判别特征集，它反映了样本集的全局结构。备用自我表示模型使用数据集本身作为稀疏表示的字典。它具有良好的类别区分能力，噪声鲁棒性和数据自适应性，增强了 FDCM_SSR 的聚类和泛化性能。在不同数据集和图像上的实验表明，FDCM_SSR 比其他最先进的模糊聚类算法更具竞争力。