Kernel principal component analysis network for image classification

Understand

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.