Modern Dimension Reduction

In the big data age, this Element offers many modern methods and opensource code for learning from high dimensional data.

Philip D. Waggoner (Author)

9781108986892, Cambridge University Press

Paperback / softback, published 5 August 2021

75 pages
22.9 x 15.2 x 0.5 cm, 0.16 kg

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.

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.

Subject Areas: Data mining [UNF], Data capture & analysis [UNC], Database design & theory [UNA], Computing & information technology [U], Society & social sciences [J], Research methods: general [GPS], Coding theory & cryptology [GPJ]