An evolution and evaluation of dimensionality reduction techniques — A comparative study

Real world data is high-dimensional like images, speech signals containing multiple dimensions to represent data. Higher dimensional data are more complex for detecting and exploiting the relationships among terms. Dimensionality reduction is a technique used for reducing complexity for analysing high dimensional data. It reduces the dimensions from the original input data. Dimensionality reduction methods can be of two types as feature extractions and feature selection techniques. Feature Extraction is a distinct form of Dimensionality Reduction to extract some important feature from input dataset. Two different approaches available for dimensionality reduction are supervised approach and unsupervised approach. One exclusive purpose of this survey is to provide an adequate comprehension of the different dimensionality reduction techniques that exist currently and also to introduce the applicability of any one of the prescribed methods that depends upon the given set of parameters and varying conditions.