基于双模糊学习的鲁棒无监督特征选择算法

高云龙; 史曙光; 赵志翔; 曹超; 潘金艳

doi:10.12263/DZXB.20240682

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基于双模糊学习的鲁棒无监督特征选择算法

学术论文 | 更新时间：2025-12-08

- 基于双模糊学习的鲁棒无监督特征选择算法
  增强出版
- Robust Unsupervised Feature Selection with Double Fuzzy Learning
- 电子学报 2025年53卷第2期页码：604-622
- 作者机构：
  
  1.厦门大学萨本栋微米纳米科学技术研究院，福建厦门 361102
  2.厦门大学健康医疗大数据国家研究院，福建厦门 361102
  3.自然资源部第三海洋研究所，福建厦门 361005
  4.集美大学海洋信息工程学院，福建厦门 361021
- 作者简介：
  
  [ "高云龙男，1979年8月出生于山西省忻州市.现为厦门大学副教授.2005年获得兰州大学计算机科学专业硕士学位.2011年获得西安交通大学控制科学与工程专业博士学位.主要研究方向为模式识别和时间序列分析. E-mail: gaoyl@xmu.edu.cn" ]
  [ "赵志翔男，2000年11月出生于内蒙古自治区包头市. 现为厦门大学硕士研究生. 主要研究方向为机器学习和数据聚类. E-mail: zhaozhixiang@stu.xmu.edu.cn" ]
  [ "史曙光男，1999年7月出生于河南省周口市.现为厦门大学硕士研究生.主要研究方向为机器学习与维数约简. E-mail: 23220231151771@stu.xmu.edu.cn" ]
  [ "曹超男，1982年1月出生于安徽省芜湖市.现为自然资源部第三海洋研究所研究员.主要研究方向为基于机器学习的海岸侵蚀机制与风险评价. E-mail: caochao@tio.org.cn" ]
  [ "潘金艳女，1978年7月出生于新疆维吾尔自治区石河子市.现为集美大学副教授.主要研究方向为最优化方法和数据挖掘. E-mail: jypan@jmu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(42076058);福建省促进海洋与渔业产业高质量发展专项资金(FJHYF-ZH-2023-05);福建省自然科学基金(2020J01713;2022J01061);广东省基础与应用基础研究基金(2024A1515011682)
- DOI：10.12263/DZXB.20240682
  中图分类号： TP391;
- 收稿：2024-07-22，
  
  修回：2024-10-12，
  
  纸质出版：2025-02-25
- 稿件说明：
移动端阅览
高云龙, 史曙光, 赵志翔, 等. 基于双模糊学习的鲁棒无监督特征选择算法[J]. 电子学报, 2025, 53(02): 604-622.

GAO Yun-long, SHI Shu-guang, ZHAO Zhi-xiang, et al. Robust Unsupervised Feature Selection with Double Fuzzy Learning[J]. Acta Electronica Sinica, 2025, 53(02): 604-622.
高云龙, 史曙光, 赵志翔, 等. 基于双模糊学习的鲁棒无监督特征选择算法[J]. 电子学报, 2025, 53(02): 604-622. DOI：10.12263/DZXB.20240682

GAO Yun-long, SHI Shu-guang, ZHAO Zhi-xiang, et al. Robust Unsupervised Feature Selection with Double Fuzzy Learning[J]. Acta Electronica Sinica, 2025, 53(02): 604-622. DOI：10.12263/DZXB.20240682

摘要

由于维数灾难问题的存在，如何在高维数据中摒弃冗余特征、保留关键信息成为一个关键问题.无监督特征选择无需任何先验类别信息就能进行维数约减的特点引起更多研究者的关注.尽管如此，现有的无监督特征选择算法仍然存在两个问题需要解决：模糊性是数据的普遍特性，但现有大多数基于正则化回归的无监督特征选择算法忽略了数据中的模糊性，导致特征子集不理想；多数算法无法有效区分正常样本和噪声样本且易受噪声的影响.针对这些问题，本文提出了基于双模糊学习的鲁棒无监督特征选择（Robust unsupervised Feature Selection with Double Fuzzy，DFRFS）算法.DFRFS算法在基于正则化回归的无监督特征选择中引入模糊隶属度，允许数据在多个簇之间共享，能够更好地反映数据的复杂结构和不确定性.此外，DFRFS算法通过鲁棒权重学习框架为样本赋予不同的权重，从而在降低噪声样本影响的同时，保留可靠样本的作用.对比相关算法，在合成数据集与真实数据集上的实验结果验证了DFRFS算法的有效性.

Abstract

Due to the curse of dimensionality

effectively discarding redundant features while retaining critical information in high-dimensional data has become a key issue. Unsupervised feature selection

which performs dimensionality reduction without any prior class information

has attracted increasing attention. However

two common issues are ignored by existing unsupervised feature selection methods: Fuzziness is a common characteristic of data

but most existing unsupervised feature selection methods based on regularized regression ignore this aspect

resulting in suboptimal feature subsets; Most methods fail to effectively distinguish between normal and noisy samples and are susceptible to the noise. To tackle the mentioned issues

robust unsupervised feature selection with double fuzzy (DFRFS) learning is proposed. Specifically

DFRFS learning introduces fuzzy membership into unsupervised feature selection based on regularized regression

allowing data to be shared among multiple clusters

thereby better reflecting the complex structure and uncertainty of the data. Additionally

DFRFS learning assigns different weights to samples through the robust weight learning framework

thus suppressing the impact of noise while retaining the effect of normal samples. Experiments on toy and real-world datasets have demonstrated the effectiveness of the proposed method DFRFS learning.

关键词

Keywords

references

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