Kernel principal component analysis
ID: kernel-principal-component-analysis
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.
New to topics? Read the docs here!