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What is Kernel PCA about?

Kernel Principal Component Analysis (KPCA) addresses the limitations of traditional PCA, particularly in terms of time complexity and non-linearity issues. It utilizes kernel functions to transform data into higher dimensions, allowing for the application of PCA in a non-linear context. The document outlines the steps for implementing KPCA, including kernel computation, centering, and eigen decomposition, while also discussing the properties of kernel functions and their validation.

Author

kaujee01

Language

EN