The K-principle component analysis dictionary learning method is proposed based on the K-singular value
decomposition(KSVD) method and the principle component analysis(PCA) method. Instead of the SVD decomposition to
the error in the KSVD method, the atoms of the dictionary of the method are updated by distilling the principle component
of the PCA decomposition. Simulation results show that, compared with the KSVD method, the better learning effect is
achieved, the representation error is small, and the learned dictionary reflects the features of the training data much better with the KPCA method.