  This paper has proposed a new baseline deep learning model of more benefits
for image classification. Different from the convolutional neural network(CNN)
practice where filters are trained by back propagation to represent different
patterns of an image, we are inspired by a method called "PCANet" in "PCANet: A
Simple Deep Learning Baseline for Image Classification?" to choose filter
vectors from basis vectors in frequency domain like Fourier coefficients or
wavelets without back propagation. Researchers have demonstrated that those
basis in frequency domain can usually provide physical insights, which adds to
the interpretability of the model by analyzing the frequencies selected.
Besides, the training process will also be more time efficient, mathematically
clear and interpretable compared with the "black-box" training process of CNN.
