  Data are not only ubiquitous in society, but are increasingly complex both in
size and dimensionality. Dimension reduction offers researchers and scholars
the ability to make such complex, high dimensional data spaces simpler and more
manageable. This Element offers readers a suite of modern unsupervised
dimension reduction techniques along with hundreds of lines of R code, to
efficiently represent the original high dimensional data space in a simplified,
lower dimensional subspace. Launching from the earliest dimension reduction
technique principal components analysis and using real social science data, I
introduce and walk readers through application of the following techniques:
locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE),
uniform manifold approximation and projection, self-organizing maps, and deep
autoencoders. The result is a well-stocked toolbox of unsupervised algorithms
for tackling the complexities of high dimensional data so common in modern
society. All code is publicly accessible on Github.
