Random Projection and Its Applications

TL;DR

This paper explains the mathematical basis, applications, and current research perspectives of Random Projection, a technique for reducing data dimensionality while preserving relative distances in machine learning.

Contribution

It provides a comprehensive overview of Random Projection's mathematical foundation, practical applications, and research directions, unifying various algorithms under a common framework.

Findings

Random Projection effectively reduces dimensionality while preserving data structure.

It has diverse applications in machine learning and data analysis.

Current research explores its theoretical limits and new applications.

Abstract

Random Projection is a foundational research topic that connects a bunch of machine learning algorithms under a similar mathematical basis. It is used to reduce the dimensionality of the dataset by projecting the data points efficiently to a smaller dimensions while preserving the original relative distance between the data points. In this paper, we are intended to explain random projection method, by explaining its mathematical background and foundation, the applications that are currently adopting it, and an overview on its current research perspective.