Nonlinear Principal Component Analysis: Tropical Indo–Pacific Sea Surface Temperature and Sea Level Pressure
Nonlinear Principal Component Analysis: Tropical Indo–Pacific Sea Surface Temperature and Sea Level Pressure
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DOI：
10.1175/1520-0442(2001)013
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发表时间：
2001-01
影响因子：
4.9
通讯作者：
A. Monahan
中科院分区：
文献类型：
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作者：
A. Monahan
摘要非线性主成分分析（NLPCA）是传统主成分分析（PCA）的推广，它可以检测和表征多元数据集的低维非线性结构。作者考虑应用NLPCA两个数据集：热带太平洋海表温度（SST）和热带印度洋-太平洋海平面气压（SLP）。结果发现，对于SST数据，低维NLPCA近似的数据特征比PCA近似相同的维数。特别是，一维NLPCA近似的特征平均厄尔尼诺和拉尼娜事件，一维PCA近似不能的空间模式特征之间的不对称性。对于SLP数据，NLPCA和PCA结果之间的差异更为适度，表明该数据集的低维结构接近线性。
Abstract Nonlinear principal component analysis (NLPCA) is a generalization of traditional principal component analysis (PCA) that allows for the detection and characterization of low-dimensional nonlinear structure in multivariate datasets. The authors consider the application of NLPCA to two datasets: tropical Pacific sea surface temperature (SST) and tropical Indo–Pacific sea level pressure (SLP). It is found that for the SST data, the low-dimensional NLPCA approximations characterize the data better than do PCA approximations of the same dimensionality. In particular, the one-dimensional NLPCA approximation characterizes the asymmetry between spatial patterns characteristic of average El Nino and La Nina events, which the 1D PCA approximation cannot. The differences between NLPCA and PCA results are more modest for the SLP data, indicating that the lower-dimensional structures of this dataset are nearly linear.