Principal Component Analysis variants for Parkinson datasets

Abstract

Principal Component Analysis (PCA) is one of the most fundamental dimension reduction methods that need further research. With the widespread popularity of machine learning and the arrival of the era of big data, dimension reduction has become a hot topic and principal component analysis is a hot topic. However, although there are a lot of researchers who focus on the methods of the PCA, few researches on Parkinson Datasets have been made. As a result, the aim of our work is to discuss the PCA variants for Parkinson Datasets. This paper first introduces the three most commonly used PCA methods: PCA, Sparse PCA and Kernel PCA, and then introduces the Support Vector Machine (SVM) used to measure the dimension reduction effect. After that, we introduced the Parkinson's dataset and the meanings of root mean square error (RMSE), overall accuracy, Cohens kappa (Kappa) and computational time, the indicators that are used to measure the dimensionality reduction effect. Finally, we identified the variants among different PCA methods on the Parkinson dataset by comparing the indicators of the data obtained after dimensionality reduction using different methods.