Dimensionality Reduction

What is Dimensionality Reduction?

The process of decreasing variables in a training dataset used to create machine learning models is known as "dimensionality reduction." By transforming high-dimensional data into a lower-dimensional space that captures the "core essence" of the data, the procedure regulates the dimensionality of the data. It is frequently utilized in disciplines like speech recognition, signal processing, bioinformatics, etc. that deal with high-dimensional data. Additionally, it can be applied to cluster analysis, noise reduction, and data visualization. For machine learning to analyze data with millions of features, numerous sources and calculations are needed. Additionally, there is a lot of manual labor involved. By transforming a high-dimensional dataset into a lower-dimensional dataset without changing the important characteristics of the original dataset, dimensionality reduction makes this difficult task comparatively simple.