无监督的特征选择,用于高阶嵌入式学习和稀疏学习
IEEE transactions on cybernetics
|March 19, 2025
概括
本研究介绍了高阶嵌入式学习和稀疏学习 (UFSHS) 的无监督特征选择. UFSHS通过使用高阶数据相似性来改善特征选择,以实现最佳的图形构造和高效的模型优化.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算统计学 计算统计学
背景情况:
- 无监督的特征选择方法往往忽略了高阶数据相似性,导致次优相似度图.
- 高复杂性和计算成本限制了现有方法的适用性,特别是对于高维数据.
研究的目的:
- 提出一种新的无监督特征选择方法,UFSHS,解决现有方法的局限性.
- 为了利用高阶相似性来构建准确的数据表示和选择最佳特征子集.
主要方法:
- UFSHS利用高阶数据相似性来构建一个最佳的相似度图,捕捉内在的几何结构.
- 统一的框架整合了高阶嵌入和稀疏学习,用于学习行-稀疏投影矩阵.
- 开发了一个新的替代优化策略,适应数据维度和实例数量,以减少计算复杂性.
主要成果:
- 拟议的UFSHS方法通过整合高阶嵌入和稀疏学习,有效地选择最佳特征子集.
- 替代优化策略显著降低了计算复杂性,并证明了对各种模型的适用性,如回归,广泛学习和模糊系统.
- 在九个公共数据集上进行了广泛的实验,证实了UFSHS与现有方法相比的优越性和效率.
结论:
- UFSHS为高维数据的无监督特征选择提供了卓越和高效的方法.
- 该方法捕捉高阶相似性的能力及其可适应的优化策略增强了其实际适用性.
- UFSHS为特征选择提供了一个强大的框架,并有可能扩展到其他机器学习模型.
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