Nonlinear component analysis as a kernel eigenvalue problem
Nonlinear component analysis as a kernel eigenvalue problem
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DOI：
10.1162/089976698300017467
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发表时间：
1998-07-01
影响因子：
2.9
通讯作者：
Muller, KR
中科院分区：
文献类型：
--
作者：
Scholkopf, B;Smola, A;Muller, KR
提出了一种非线性主成分分析的新方法。通过使用积分算子核函数，可以有效地计算高维特征空间中的主成分，这些特征空间与某些非线性映射的输入空间相关，例如，16 x 16图像中所有可能的五像素积的空间。给出了该方法的推导过程，并给出了模式识别中多项式特征提取的实验结果。
A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear map-for instance, the space of all possible five-pixel products in 16 x 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.