Gunler Pirim M Altinay, Tora Hakan, Oztoprak Kasim, Butun İsmail
Vakifbank, 06200 Ankara, Turkey.
Department of Avionics, Atilim University, 06830 Ankara, Turkey.
Sensors (Basel). 2023 Oct 15;23(20):8477. doi: 10.3390/s23208477.
In this paper, a novel feature generator framework is proposed for handwritten digit classification. The proposed framework includes a two-stage cascaded feature generator. The first stage is based on principal component analysis (PCA), which generates projected data on principal components as features. The second one is constructed by a partially trained neural network (PTNN), which uses projected data as inputs and generates hidden layer outputs as features. The features obtained from the PCA and PTNN-based feature generator are tested on the MNIST and USPS datasets designed for handwritten digit sets. Minimum distance classifier (MDC) and support vector machine (SVM) methods are exploited as classifiers for the obtained features in association with this framework. The performance evaluation results show that the proposed framework outperforms the state-of-the-art techniques and achieves accuracies of 99.9815% and 99.9863% on the MNIST and USPS datasets, respectively. The results also show that the proposed framework achieves almost perfect accuracies, even with significantly small training data sizes.
本文提出了一种用于手写数字分类的新型特征生成器框架。所提出的框架包括一个两阶段级联特征生成器。第一阶段基于主成分分析（PCA），它生成主成分上的投影数据作为特征。第二阶段由一个部分训练的神经网络（PTNN）构建，该网络使用投影数据作为输入并生成隐藏层输出作为特征。从基于PCA和PTNN的特征生成器获得的特征在为手写数字集设计的MNIST和USPS数据集上进行测试。最小距离分类器（MDC）和支持向量机（SVM）方法被用作与该框架相关联的所获得特征的分类器。性能评估结果表明，所提出的框架优于现有技术，在MNIST和USPS数据集上分别达到了99.9815%和99.9863%的准确率。结果还表明，即使训练数据量非常小，所提出的框架也能实现几乎完美的准确率。
