The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

In the era of healthcare, and its related research fields, the dimensionality problem of high
dimensional data is a massive challenge as it contains a huge number of variables forming …

Dimensionality Reduction (DR) is attracting more attention these days as a result of the
increasing need to handle huge amounts of data effectively. DR methods allow the number …

High-dimensional data is ubiquitous in scientific research and industrial production fields. It
brings a lot of information to people, at the same time, because of its sparse and …

Advances in data collection and storage capabilities during the past decades have led to an
information overload in most sciences. Researchers working in domains as diverse as …

Dimension reduction is defined as the processes of projecting high-dimensional data to a
much lower-dimensional space. Dimension reduction methods variously applied in …

Dimensionality reduction is one basic and critical technology for data mining, especially in
current “big data” era. As two different types of methods, feature selection and feature …

Dimensionality reduction is an important task in machine learning, for it facilitates
classification, compression, and visualization of high-dimensional data by mitigating …

As basic research, it has also received increasing attention from people that the “curse of
dimensionality” will lead to increase the cost of data storage and computing; it also …

Purpose: This study sought to review the characteristics, strengths, weaknesses variants,
applications areas and data types applied on the various Dimension Reduction techniques …