A survey of dimensionality reduction techniques

TL;DR

This survey reviews various dimensionality reduction techniques used in life sciences, categorizing methods and explaining their mathematical foundations to address high-dimensional data challenges.

Contribution

It provides a comprehensive categorization and mathematical overview of dimensionality reduction techniques, aiding researchers in selecting appropriate methods.

Findings

Many techniques effectively reduce data dimensionality with minimal information loss

Categorization helps clarify the landscape of available methods

Mathematical insights facilitate understanding and application

Abstract

Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and therefore the statistical methods face challenging tasks when dealing with such high dimensional data. However, much of the data is highly redundant and can be efficiently brought down to a much smaller number of variables without a significant loss of information. The mathematical procedures making possible this reduction are called dimensionality reduction techniques; they have widely been developed by fields like Statistics or Machine Learning, and are currently a hot research topic. In this review we categorize the plethora of dimension reduction techniques available and give the mathematical insight behind them.