Which technique is primarily used to reduce dimensionality in datasets?

Principal Component Analysis (PCA) is a statistical technique widely used for dimensionality reduction in datasets. It works by identifying the most important features or components of the data while minimizing information loss. By transforming the data into a new coordinate system where the greatest variance from the original data is captured in the first few dimensions, PCA enables the retention of relevant patterns and relationships while discarding less significant information. This reduction in the number of dimensions helps simplify models, reduce computational costs, and improve visualization.

In contrast, clustering is a technique used to group similar data points together based on their characteristics, but it does not specifically focus on reducing the number of dimensions in a dataset. Regression aims to model the relationship between a dependent variable and one or more independent variables, not to reduce dimensionality directly. Normalization is a preprocessing step that adjusts the range of data values to a common scale, but it does not inherently reduce the number of features or dimensions in the dataset. Hence, PCA is the best choice for explicitly addressing dimensionality reduction.