Optimization of MNIST Handwritten Digit Recognition Based on PCA Dimensionality Reduction
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.
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