  An efficient, scalable and robust approach to the handwritten digits
recognition problem based on the Saak transform is proposed in this work.
First, multi-stage Saak transforms are used to extract a family of joint
spatial-spectral representations of input images. Then, the Saak coefficients
are used as features and fed into the SVM classifier for the classification
task. In order to control the size of Saak coefficients, we adopt a lossy Saak
transform that uses the principal component analysis (PCA) to select a smaller
set of transform kernels. The handwritten digits recognition problem is well
solved by the convolutional neural network (CNN) such as the LeNet-5. We
conduct a comparative study on the performance of the LeNet-5 and the
Saak-transform-based solutions in terms of scalability and robustness as well
as the efficiency of lossless and lossy Saak transforms under a comparable
accuracy level.
