Performance Evaluation of Feature Extraction and Dimensionality Reduction Techniques on Various machine learning classifiers

Abstract

This paper represents handwritten digit recognition on a very well-known dataset which is MNIST dataset using the Linear Binary Pattern (LBP) and Scale-Invariant Feature Transform (SIFT) feature extraction methods. From the dataset, features have been extracted using this extracting methods. After this, to reduce the number of features or reducing the dimension we have used Principal Component Analysis (PCA) for better performance for our proposed classifier. Then it has been trained by various classifiers. Then the accuracies and errors of those classifiers have been compared and demonstrated. Also some statistical analysis has been done for better understanding of the dataset. From those comparison, it has been shown that our proposed model (SIFT+PCA+CNN) has the better accuracy and less error than other classifiers. The results are competitively compared to previous works and they provide a baseline for evaluation of future work.