首先利用PCA对每位注册说话人的特征向量进行维数约简,由转换矩阵得到每位说话人的主成分空间（principal component space,PCS）,在此空间上快速判断出可能的R个说话人;然后在R个可能说话人的约简向量集上建立高斯混合模型;最后利用...
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研究了各主成分的空间分布特征、意义以及月均值随时间的变化规律。
Besides, the spatial distribution characteristics and significance of the principal components and the variation of their monthly averaged values were studied.
结果表明：祁连山附近气温在空间上具有很好的一致性，年平均气温的第一主成分的方差贡献可占总方差的75%左右。
The results show temperature spatial variations have high consistency, the variance ratio contribution of the first principal component is about 75%.
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。
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.
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