Machine learning model is capable of achieving an incredible great accuracy by training a large amount of data with many dimensions of features in it. To accomplish such a goal, we are currently in desperate need of powerful computers with CPUs (Computation Processing Unit) and GPUs (Graphic Processing Unit) that are competent of the workload of the training process of those machine learning models. Due to the problem that there are multiple disparate computationally intensive tasks that are lengthy period of time during training phase of machine learning, devices like smartphone and personal computer cannot achieve great efficiency during the training phase of machine learning. Instead of enhancing computational performance of devices and improve the machine learning algorithm, another more feasible way to bypass such problem is to apply dimensionality reduction methods on raw data in hope of reducing the number of features that are passing into the machine learning model during the training phase. By doing so, we can shrink the originally large number of features to several dimensions that is accomplishable for our device to compute and meanwhile, sufficient enough to have a model with acceptable accuracy trained. The purpose of this study to explore and investigate the potential benefits and disadvantages of dimensionality reduction on multiple distinct machine learning algorithm by comparing the performances of each machine learning algorithms when passing the features of raw datasets in and when passing the dataset with the features that survived after dimensionality reduction. The performance will be measured by completing a typical machine learning task: handwriting digit classification. Among all the dimensionalities methods that have been discovered, a traditional and conventional dimensionality reduction method, PCA (Principal Components Analysis), is selected. Such method has been proven successful and efficient in generating the set of data with lower dimension features and apply on an image classification task using several supervised machine learning methods, including KNN (k-nearest neighbors), SVM (Support vector machine) and CNN (Convolutional Neural Network).

Yingqiang Yuan. Handwriting digit classification using PCA-transformed image features. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 6: 78-81. https://doi.org/10.25236/AJCIS.2021.040613.

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