Dimensionality Reduction: Challenges and Solutions

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