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Updated: Jan 15, 2026

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核心化的线性主要组件差异分析

Lingxiao Qu1, Yan Pei2

  • 1Graduate School of Computer Science and Engineering, University of Aizu, Itsukimachi Oaza Tsuruga, Kamiiawase 90, Aizuwakamatsu, Fukushima, 965-0006, Japan.

Neural networks : the official journal of the International Neural Network Society
|January 13, 2026
PubMed
概括

核心化的线性主要组件歧视分析 (KLPCDA) 统一了特征提取和类歧视. 这种新的框架提高了差别分析的性能,特别是在小样本尺寸的环境中.

关键词:
差异化分析是一种差异化分析.核聚变技术是可以实现的.核心方法 核心方法在RKHSHS中.一个小样本的样本大小很小.

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