# A Saak Transform Approach to Efficient, Scalable and Robust Handwritten Digits Recognition

arXiv: https://arxiv.org/abs/1710.10714 · HTML: https://mlsanity.com/paper/1710.10714/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2017.

## Built on by (most cited first, 7 of 7)

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- [Learn to Enhance the Negative Information in Convolutional Neural Network](https://mlsanity.com/paper/2306.10536/) (2023)
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## Abstract (arXiv)

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.