Dimensionality Reduction for Handwritten Digit Recognition

Human perception of dimensions is usually limited to two or three degrees. Any further increase in the number of dimensions usually leads to the difficulty in visual imagination for any person. Hence, machine learning researchers often commonly have to overcome the curse of dimensionality in high dimensional feature sets with dimensionality reduction techniques. In this proposed model, two handwritten digit datasets are used: CVL Single Digit and MNIST, and two popular feature descriptors, Histogram of Oriented Gradients (HOG) and Gabor filters, are used to generate the feature sets. Investigations are carried out on linear and nonlinear transformations of the feature sets using multiple dimensionality reduction techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Isomap. The lower dimension vectors obtained, are then used to classify the numeric digits using Support Vector Machine (SVM). A conclusion arrived is that using HOG as the feature descriptor and PCA as the dimensionality reduction technique resulted in the experimental model achieving the highest accuracy of 99.29% on the MNIST dataset with the time efficiency comparable to that of a convolutional neural network (CNN). Further, it is concluded that even though the LDA model with HOG as the feature descriptor achieved a lesser accuracy of 98.34%, but it was able to capture maximum information in just 9 components in its lower dimensional subspace with 75% reduction in time efficiency of that of the PCA-HOG model and the CNN model.