Dimensionality Reduction: Challenges and Solutions

Abstract

The use of dimensionality reduction techniques is a keystone for analyzing and interpreting high dimensional data. These techniques gather several data features of interest, such as dynamical structure, input-output relationships, the correlation between data sets, covariance, etc. Dimensionality reduction entails mapping a set of high dimensional data features onto low dimensional data. Motivated by the lack of learning models’ performance due to the high dimensionality data, this study encounters five distinct dimensionality reduction methods. Besides, a comparison between reduced dimensionality data and the original one using statistical and machine learning models is conducted thoroughly.