Additionally, dimensionality reduction techniques can aid in data visualization, as they enable the representation of complex datasets in a more manageable and interpretable manner. It allows us to understand the underlying structure or patterns in the data, aiding in the decision-making process.

Dimensionality reduction techniques like Principal Component Analysis (PCA) and t-SNE can be applied in Machine Learning to reduce the number of features in a dataset. This is especially useful when dealing with high-dimensional data, as it helps to eliminate irrelevant or redundant features, improve model performance, and visualize the data in a lower-dimensional space.

Dimensionality reduction can also be used for feature extraction, where new representative features are created using a combination of the original features. This can help in capturing the most important aspects of the data, reducing noise, and improving the efficiency of the learning algorithm.