  In order to classify the nonlinear feature with linear classifier and improve
the classification accuracy, a deep learning network named kernel principal
component analysis network (KPCANet) is proposed. First, mapping the data into
higher space with kernel principal component analysis to make the data linearly
separable. Then building a two-layer KPCANet to obtain the principal components
of image. Finally, classifying the principal components with linearly
classifier. Experimental results show that the proposed KPCANet is effective in
face recognition, object recognition and hand-writing digits recognition, it
also outperforms principal component analysis network (PCANet) generally as
well. Besides, KPCANet is invariant to illumination and stable to occlusion and
slight deformation.
