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Updated: May 25, 2025

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基于布尔权重的双高阶图形学习的子空间学习

Yilong Wei1, Jinlin Ma2, Ziping Ma1

  • 1School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China.

Entropy (Basel, Switzerland)
|February 26, 2025
PubMed
概括

本研究介绍了用于双高阶图形学习 (DHBWSL) 的子空间学习,通过考虑样本和特征关系来增强无监督特征选择. DHBWSL有效地保留了当地的几何数据特征,优于现有方法.

关键词:
布尔式权重是布尔式的权重.亚空间学习是指子空间学习.双高阶图形学习的双高阶图形学习无监督的特征选择选择.

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
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