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《山东大学学报(理学版)》 ›› 2026, Vol. 61 ›› Issue (3): 124-134.doi: 10.6040/j.issn.1671-9352.0.2024.118

• • 上一篇    

直觉模糊局部保持投影最小二乘双支持向量聚类

王顺霞1,黄成泉2*,蔡江海1,杨贵燕1,罗森艳1,周丽华1   

  1. 1.贵州民族大学数据科学与信息工程学院, 贵州 贵阳 550025;2.贵州民族大学工程技术人才实践训练中心, 贵州 贵阳 550025
  • 发布日期:2026-03-18
  • 通讯作者: 黄成泉(1976— ),男,教授,硕士生导师,博士,研究方向为模式识别、机器学习与图像处理等. E-mail: hcq863@163.com
  • 作者简介:王顺霞(1999— ),女,硕士研究生,研究方向为机器学习与模式识别. E-mail:2689826749@qq.com*通信作者:黄成泉(1976— ),男,教授,硕士生导师,博士,研究方向为模式识别、机器学习与图像处理等. E-mail: hcq863@163.com
  • 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)

Intuitionistic fuzzy locality preserving projection least squares twin support vector clustering

WANG Shunxia1, HUANG Chengquan2*, CAI Jianghai1, YANG Guiyan1, LUO Senyan1, ZHOU Lihua1   

  1. 1. School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, Guizhou, China;
    2. Engineering Training Center, Guizhou Minzu University, Guiyang 550025, Guizhou, China
  • Published:2026-03-18

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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。

关键词: 双支持向量, 聚类, 局部保持投影, 直觉模糊, 噪声

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.

Key words: twin support vector, clustering, locality preserving projection, intuitionistic fuzzy, noise

中图分类号: 

  • TP181
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