Abstract
In the analysis and processing of image recognition, extracting useful and valuable data from the original dataset has become a problem. Since the data to be processed often presents high dimensional and nonlinear feature, reasonable dimensionality reduction is an necessary method for improving the accuracy of data analysis. One of the dimensionality reduction methods Kernel Principal Component Analysis (KPCA) has certain advantages in dealing with nonlinear data, but it also has defects when facing the dataset in which data owe highly complex relationship. The other linear dimensional reduction method Linear Discriminant Analysis (LDA) has supervisory characteristics, which can reduce the dimensionality of dataset in which data owe highly complex relationship. However it can only handle linear data. So we propose a hybrid method which is the combination of the above two methods called KPCA-LDA. By it the new dataset obtained by the step of dimensionality reduction is beneficial to be processed in next step for classification. We combine KPCA-LDA with the Back Propagation Neural Network (BPNN) method to achieve the classification of handwritten numbers. The experimental results show that the classification accuracy of the proposed KPCA-LDA-BPNN model can reach 98.67%, which is about 3%-5% higher than the original method using K Nearest Neighbor (KNN) and Support Vector Machine (SVM).
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Acknowledgement
The research is supported by Natural Science Foundation of China under Grant No. 61662054, 61262082, Inner Mongolia Science and Technology Innovation Team of Cloud Computing and Software Engineering and Inner Mongolia Application Technology Research and Development Funding Project “Mutual Creation Service Platform Research and Development Based on Service Optimizing and Operation Integrating” under Grant 201702168, Inner Mongolia Engineering Lab of Cloud Computing and Service Software and Inner Mongolia Engineering Lab of Big Data Analysis Technology.
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Zhao, Z., Zhou, J., Xing, H. (2019). A High Accuracy Nonlinear Dimensionality Reduction Optimization Method. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_55
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