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基于监督区分投影的高维网络数据的特征选择算法的设计.

Zongfu Zhang1, Qingjia Luo2, Zuobin Ying2

  • 1College of Information Engineering, Jiangmen Polytechnic, Jiangmen, China.

PeerJ. Computer science
|July 6, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种用于高维网络数据的新型特征选择算法,提高了准确性和效率. 监督区分投影 (SDP) 方法有效处理复杂的数据,实现高性能指标.

关键词:
功能选择 功能选择网络的高维数据数据.稀少的约束限制.稀有的子空间聚类.监督的歧视性投影.

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科学领域:

  • 数据科学数据科学数据科学
  • 网络分析 网络分析
  • 机器学习 机器学习

背景情况:

  • 由于其复杂性,高维网络数据对有效的特征选择提出了挑战.
  • 现有的方法经常与网络数据的规模和复杂性作斗争,导致功能识别不足最佳.

研究的目的:

  • 为高维网络数据开发一个有效的特征选择算法.
  • 解决当前处理复杂网络结构和大型数据集的方法的局限性.

主要方法:

  • 基于监督区分投影 (SDP) 的设计特征选择算法.
  • 将稀疏表示问题转化为Lp规范优化问题.
  • 利用稀疏的子空间聚类和无维处理与SDP相结合,用于特征减少和选择.

主要成果:

  • 该算法有效地聚合了七种不同的数据类型,围绕24次代汇聚在一起.
  • 取得了高水平的F1得分,回忆和精度.
  • 显示了96.9%的平均特征选择精度,平均时间为65.1毫秒.

结论:

  • 拟议的算法为在高维网络数据中的特征选择提供了强大的解决方案.
  • 该方法在增强数据聚类和实现准确的特征识别方面被证明是有效的.
  • 与现有方法相比,该算法在准确性和效率上都显示出显著的改进.