A Saak Transform Approach to Efficient, Scalable and Robust Handwritten Digits Recognition
Yueru Chen, Zhuwei Xu, Shanshan Cai, Yujian Lang, C.-C. Jay Kuo

TL;DR
This paper introduces a Saak transform-based method for handwritten digits recognition that is scalable, efficient, and robust, offering an alternative to traditional CNN approaches like LeNet-5.
Contribution
It proposes a novel multi-stage Saak transform approach with PCA-based kernel selection for improved recognition performance and efficiency.
Findings
Saak transform achieves comparable accuracy to CNNs.
Lossy Saak transform reduces feature size with minimal accuracy loss.
Method demonstrates enhanced scalability and robustness.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Applications · Image and Signal Denoising Methods · Blind Source Separation Techniques
MethodsSupport Vector Machine
