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。
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。
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。
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