Dimensionality reduction is a central method in the field of Data Analysis and Machine Learning that makes it possible to reduce the number of dimensions in a data set while retaining as much of the information it contains as possible. This step is necessary to reduce the dimensionality of the dataset before training to save computing power and avoid the problem of overfitting.
In this article, we take a detailed look at dimensionality reduction and its objectives. We also illustrate the most commonly used methods and highlight the challenges of dimensionality reduction.
Dimensionality reduction comprises various methods that aim to reduce the number of characteristics and variables in a data set while preserving the information in it. In other words, fewer dimensions should enable a simplified representation of the data without losing patterns and structures within the data. This can significantly accelerate downstream analyses and also optimize machine learning models.
