A Review on Dimension Reduction Methods

Dimension reduction (DR) is the process of reducing the number of variables (also sometimes referred to as features or of course dimensions) to a set of values of variables called principal variables. The main property of principal variables is the preservation of the structure and information carried by the original variables, to some extent. Principal variables are ordered by importance, with the first variable preserving the most structure and following variables preserving successively less. DR is a widely used approach to find low dimensional and interpretable representations of data that are natively embeddedin high-dimensional spaces. DR can be realized by a plethora of methods with different properties, objectives, and, hence, (dis)advantages. This document is based on [17] and [10].

Keywords:

Dimension Reduction , High Dimensional , Principal Variables.