现代信息科技2024，Vol.8Issue(16)：64-68,5.DOI:10.19850/j.cnki.2096-4706.2024.16.014
基于PCA降维的MNIST手写数字识别优化
Optimization of MNIST Handwritten Digit Recognition Based on PCA Dimensionality Reduction
摘要
Abstract
PCA data dimensionality reduction technology is widely used in data dimensionality reduction and feature extraction,which can greatly reduce the computational complexity of algorithms and improve program efficiency.This paper takes the MNIST original dataset and the dataset after PCA dimensionality reduction as samples,and uses K-Nearest Neighbor algorithm,Decision Tree ID3 algorithm,SVC classification model,as well as Ensemble Learning methods that select different classification algorithms as basic classifiers to achieve handwritten digit recognition.It compares and analyzes the time complexity and prediction accuracy of different classification algorithms and models before and after PCA dimensionality reduction on the MNIST dataset,further enhances and optimizes various indicators such as handwritten digit recognition accuracy.关键词
PCA降维/MNIST手写数字识别/K-邻近算法/决策树/SVC分类模型/集成学习Key words
PCA dimensionality reduction/MNIST handwritten digit recognition/K-Nearest Neighbor algorithm/Decision Tree/SVC classification model/Ensemble Learning分类
信息技术与安全科学引用本文复制引用
田春婷..基于PCA降维的MNIST手写数字识别优化[J].现代信息科技,2024,8(16):64-68,5.基金项目
甘肃省教育厅高校教师创新项目(2023A-205) （2023A-205）
