Mahmoud Rokaya*

Department of Information Technology, Taif University, Saudi Arabia

*Corresponding Author: Mahmoud Rokaya, Department of Information Technology, Taif University, Saudi Arabia.

Received: November 14, 2022; Published: December 23, 2022

Abstract

In the current decade, most of the computational problems came to be problems with high dimensional data. Correct data reduction will relax a load of computation to an acceptable range in time and space. Most of the available data reduction methods are built on the statistical background. Few of them adopted machine learning. Few works considered ensemble learning as a method to merge different methods to get a superior method to all merged individual reduction methods. This work will present the history of high-dimensional data reduction methods. It will analyze the recent developments in methods of reduction data schemes, especially ensemble methods.

Keywords: Dimensionality Reduction; Random Projection; PCA; Curvilinear Component Analysis (CCA); Projected Support Points (PSPs); Sequential Ensemble (SEMSE); Projection Pursuit; Particle Swarm Optimization (PSO); Genetic Algorithm (GA); Quadratic Discriminant Analysis (QDA)

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Citation

Citation: Mahmoud Rokaya. “Review of High-Dimensional Data Reduction Methods".Acta Scientific Computer Sciences 5.1 (2023): 94-101.

Copyright

Copyright: © 2023 Mahmoud Rokaya. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.