which these concentric point clouds are not linearly separable. Applications Kernel PCA has been demonstrated to be useful for novelty detection and image de-noising. See also Cluster analysis Nonlinear dimensionality reduction Spectral clustering References Dimension reduction Signal processing Machine learning algorithms Kernel methods for machine learning sv:Principalkomponentanalys#Olinjär PCA