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Nonlinear Component Analysis as a Kernel Eigenvalue Problem
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DOI：10.1162/089976698300017467.png)
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Abstract
En 中文
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 × 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.
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