  The principal component analysis network (PCANet), which is one of the
recently proposed deep learning architectures, achieves the state-of-the-art
classification accuracy in various databases. However, the explanation of the
PCANet is lacked. In this paper, we try to explain why PCANet works well from
energy perspective point of view based on a set of experiments. The impact of
various parameters on the error rate of PCANet is analyzed in depth. It was
found that this error rate is correlated with the logarithm of energy of image.
The proposed energy explanation approach can be used as a testing method for
checking if every step of the constructed networks is necessary.
