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Kernel principal component analysis

 Home Mathematics Fields of mathematics Applied mathematics Algorithms Machine learning algorithms
 0 By others on same topic  0 Discussions  1970-01-01  See my version
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.

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