Dimensionality reduction is the process of reducing the number of features or dimensions in a dataset. This can be useful for reducing the complexity of a dataset and making it easier to work with. Here is an example of code implementing two common dimensionality reduction algorithms, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), in C++.