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An in-depth article about downsizing and its most popular methods

Dimensionality reduction is a central technique in the field of data analysis and machine learning that makes it possible to reduce the number of dimensions in a data set while preserving as much information as possible. This step is necessary to reduce the size of the dataset before training in order to save computing power and avoid the problem of overfitting.

In this article, we take a closer look at downsizing and its goals. We also show the most commonly used methods and highlight the challenges of dimensionality reduction.

Dimensionality reduction includes various methods that aim to reduce the number of features and variables in a data set while preserving information from it. In other words, the few dimensions should enable a simplified representation of the data without losing the patterns and structures within the data. This can greatly speed up downstream analysis and also improve machine learning models.