This model constructs two feature extractors based on Kernel Principle Component Analysis(KPCA) and Kernel Independent Component Analysis(KICA), and uses a novel ensemble approach to learn the results produced by the extractors.
该模型分别构造基于核主成分分析(KPCA)和核独立成分分析(KICA)的特征提取器，并采用集成学习对特征提取结果进行整合学习。
参考来源 - 基于分布式集成学习的入侵检测模型·2,447,543篇论文数据，部分数据来源于NoteExpress
Base on these, we propose a kernel function include fractional inner-product model which is better fulfill these properties, and apply it to kernel principle component analysis.
在此基础上，提出了更好的满足这些性能的小指数点积核函数，并将应用到主分量分析中。
In this paper, kernel independent component analysis (KICA) 's principle and algorithm are introduced, and then the KICA comparison with some other ICA and principal component analysis (PCA) is given.
论文介绍了基于核空间的ICA的原理和基本算法，然后介绍了该算法与典型ICA和主成分分析(PCA)在盲源信号分离中的比较。
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