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Series:   Elements in Quantitative and Computational Methods for the Social Sciences

Modern Dimension Reduction

Published online by Cambridge University Press:  10 July 2021

Philip D. Waggoner
Philip D. Waggoner
Affiliation:
University of Chicago

Summary

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.
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Keywords

dimension reductionunsupervised machine learningcomputational social sciencebig datasocial computing
Type
Element
Information
Series: Elements in Quantitative and Computational Methods for the Social Sciences
Online ISBN: 9781108981767
Publisher: Cambridge University Press
Print publication: 05 August 2021

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