  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.
