基于双重约束的半监督深度聚类方法
Mona Suliman AlZuhair1, Mohamed Maher Ben Ismail1, Ouiem Bchir1
1Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究引入了一种新的半监督深度集群方法,使用双重约束来改善数据分区. 基于双约束的半监督深度聚类 (DC-SSDEC) 方法提高了对基准数据集的聚类准确性.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 与无监督方法相比,半监督的集群方法是有限的.
- 现有的方法难以避免局部最小值,并有效地完善数据集群.
研究的目的:
- 引入一种新的半监督深度聚类方法.
- 利用深度神经网络和模糊的会员资格来改进数据分区.
主要方法:
- 拟议的基于双约束的半监督深度集群 (DC-SSDEC) 方法.
- 使用"应该链接"和"不应该链接"的双对软约束.
- 制定了集群任务作为一个新的目标函数的优化.
主要成果:
- 与最先进的集群技术相比,DC-SSDEC表现出更高的性能.
- 与单一约束方法相比,实现了3.25% (MNIST),1.44% (STL-10) 和1.82% (USPS) 的精度改进.
- 通过对现实世界和基准数据集的全面实验进行验证.
结论:
- 拟议的双约束配方和新的目标功能显著提高了集群性能.
- DC-SSDEC为半监督深度集群提供了一个强大的方法.
- 该方法在现实世界数据集上显示了实际适用性.
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