Dimensionality Reduction

both curse [8]-a blessing because it implies a lot of information, and a curse because the data might contain a lot of irrelevant information and be tougher to analyze. High-dimensional data is known to pose a wide variety of challenges in the field of statistics. Dimensionality Reduction, or DR, is therefore employed to deal with the issue of high-dimensional data. Specifically, DR is primarily applied when there is a need to: 1. Select only relevant features from the given data (feature selection) or 2. Extract lower-dimensional data from the current form of the data because the current form is too difficult to analyze (feature extraction).