Dimensionality Reduction for Handwritten Digit Recognition

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Dimensionality Reduction for Handwritten Digit Recognition

Journal Title: EAI Endorsed Transactions on Cloud Systems - Year 2018, Vol 4, Issue 13

Abstract

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.

Authors and Affiliations

Ankita Das, Tuhin Kundu, Chandran Saravanan

Keywords

Dimensionality Reduction Feature Descriptors HOG Gabor PCA LDA Isomap SVM Classification

EP ID EP45609
DOI http://dx.doi.org/10.4108/eai.12-2-2019.156590
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