基于模糊图的强大的无监督特征选择算法
Zhouqing Yan1, Ziping Ma1,2, Jinlin Ma3
1School of Mathematics and Information Science, North Minzu University, Yinchuan 750030, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
一个新的模糊图算法 (FWFGFS) 通过结合模糊数据信息来增强无监督的特征选择. 这种方法提高了集群精度,并减少了噪声影响,从而更好地选择特征子集.
科学领域:
- 机器学习
- 数据挖掘
- 模式识别
背景情况:
- 无监督的特征选择识别了没有标签的最佳特征子集.
- 现有的方法与模糊的数据信息和噪声作斗争,影响集群结构建模.
- 重建中的二次误差加剧了当前方法中的噪音敏感性.
研究的目的:
- 提出一个强大的无监督特征选择算法,FWFGFS,使用模糊图.
- 通过有效地建模模糊的集群结构和减轻噪声来解决现有方法的局限性.
- 在未标记的数据中提高特征选择的准确性和稳定性.
主要方法:
- 开发一个模糊的图学习机制,用于软集群分配模糊的成员分布.
- 引入适应性模糊权重机制以减少冗余功能的噪音和错误.
- 在独立集群中心的低维表示中应用直角三元化.
主要成果:
- FWFGFS有效地模拟模糊的邻居关系,提高集群精度.
- 适应权重机制减少了特征选择中的噪声干扰.
- 实验结果显示,对12个数据集的平均聚类精度 (5.68%13.79%) 与最新的方法相比显著改善.
结论:
- 通过利用模糊信息,FWFGFS提供了强大而准确的无监督特征选择方法.
- 拟议的机制增强了集群结构建模和噪声弹性.
- FWFGFS代表了未标记数据分析的特征选择的重大进步.
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