Kernel principal component analysis diagonalizes a centered kernel matrix to perform principal component analysis in an implicit feature space.
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Kernel Principal Component Analysis (KPCA) is a non-linear extension of Principal Component Analysis (PCA) that uses kernel methods to transform data into a higher-dimensional space. This transformation allows for the extraction of principal components that can capture complex, non-linear relationships in the data.