Modern Dimension Reduction

TL;DR

This paper provides an overview of modern unsupervised dimension reduction techniques, including practical R code implementations, to help manage and analyze high-dimensional data in social science and other fields.

Contribution

It introduces and demonstrates a suite of recent dimension reduction methods with accessible code, enhancing practical tools for high-dimensional data analysis.

Findings

Provides a comprehensive toolbox of dimension reduction techniques

Includes practical R code for each method

Demonstrates application on real social science data

Abstract

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.