A SURVEY OF DIMENSIONALITY REDUCTION AND CLASSIFICATION METHODS

Abstract

Dimensionality Reduction is usually achieved on the feature space by adopting any one of the prescribed methods that fall under the selected technique. Feature selection and Feature extraction being the two aforesaid techniques of reducing dimensionality, the former discards certain features that may be useful at a later stage whereas the latter re-constructs its features into a simpler dimension thereby preserving all its initial characteristics. The sole 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 depending upon the given set of parameters and varying conditions as described, under each algorithm’s usage statistics. This paper also presents guidelines where in, selection of the best possible algorithm for a specific instance can be determined with ease when a condition arises where in two or more algorithms may be suitable for executing the aforementioned task.