Performance Analysis of Dimensionality Reduction Techniques: A Comprehensive Review

Abstract

Dimensionality reduction is comprised of techniques which are applied for lessening the dimensions of high-dimensional data. When there is increment in the data size and in its characteristics, then dimensionality reduction is applied to convert dataset into lesser dimensions with keeping the information short and precise. In other words, the process maintains the conciseness of the information without loss. The definition for dimension reduction can be stated as reducing n dimensional data for n dimensional space to i dimensional dimensions where i < n. To fulfill this objective, few techniques which are combination of mathematics and statistics are applied for dimensionality reduction are discussed in the paper. The paper focusses on the concept, techniques, and applications of dimensionality reduction. Additionally, the aim of the paper is to analyze and compare different techniques of dimensionality reduction with visualized results. Among different techniques of dimensionality reduction, results visualize LDA to be more informative and accurate.