Gradient Descent, as it optimizes the model parameters to minimize the loss function, indirectly reducing dimensionality by focusing on the most relevant features.

Random Forest, which can inherently select important features during the construction of decision trees, thus reducing dimensionality.

Principal Component Analysis (PCA), a technique designed to transform a large set of variables into a smaller one that still contains most of the information in the large set.

K-Nearest Neighbors (KNN), which reduces dimensionality by considering only the closest neighbors in the feature space for making predictions.