直觉模糊局部保持投影最小二乘双支持向量聚类

Intuitionistic fuzzy locality preserving projection least squares twin support vector clustering

摘要

针对数据样本的局部结构信息未得到充分利用以及算法对噪声的敏感特性导致聚类效果下降的问题,提出直觉模糊局部保持投影最小二乘双支持向量聚类方法。基于样本与质心的距离以及样本的异质性分配模糊得分,给样本赋予权重,充分利用训练样本的局部几何结构信息,提供样本邻域的先验信息,不仅降低了噪声和异常值对算法性能的影响,而且有效解决了数据的聚类问题。在多个数据集上进行实验,并通过统计分析检验所提算法的显著性,实验结果证实了所提算法比其它现有算法具有更好的鲁棒性能和聚类性能。

Abstract

To solve the problem that the local structure information of data samples is not fully utilized and the sensitivity of the algorithm to noise leads to the decline of the clustering effect, this paper proposes the intuitionistic fuzzy local preserving projection least square twin support vector clustering method. Fuzzy scores are assigned based on the distance between samples and centroids and the heterogeneity of the samples, given weights to the samples, and the local geometric structure information of the training sample is fully utilized to provide prior information about the sample neighborhood, which not only reduces the influence of noise and outliers on the performance of the algorithm, but also effectively solves the clustering problem of data. Experiments are conducted on several datasets, and the significance of the proposed algorithm is verified by statistical analysis. These experimental results demonstrate that the proposed algorithm has better robustness and clustering performance than other existing algorithms.

关键词

双支持向量 / 聚类 / 局部保持投影 / 直觉模糊 / 噪声

Key words

twin support vector / clustering / locality preserving projection / intuitionistic fuzzy / noise

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基金资助

国家自然科学基金资助项目(62062024)

贵州省模式识别与智能系统重点实验室2022年度开放课题资助项目(GZMUKL[2022]KF03)

贵州省省级科技计划资助项目(黔科合基础-ZK[2021]一般342)

贵州省教育厅自然科学研究资助项目(黔教技[2022]015)