Modern Dimension Reduction

Philip D. Waggoner (University of Chicago)

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English

Cambridge University Press
05 August 2021

Coding theory & cryptology; Research methods: general; Database design & theory; Data capture & analysis; Data mining

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

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.

By: Philip D. Waggoner (University of Chicago)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 229mm, Width: 152mm, Spine: 5mm
Weight: 160g
ISBN: 9781108986892
ISBN 10: 1108986897
Series: Elements in Quantitative and Computational Methods for the Social Sciences
Pages: 75
Publication Date: 05 August 2021
Audience: Professional and scholarly , College/higher education , Undergraduate , Further / Higher Education
Format: Paperback
Publisher's Status: Active

1. Introduction; 2. A Classic Approach to Dimension Reduction; 3. Locally Linear Embedding; 4. Nonlinear Dimension Reduction for Visualization; 5. Neural Network-Based Approaches; 6. Final Thoughts on Dimension Reduction.