Modern Dimension Reduction
Seiten
2021
Cambridge University Press (Verlag)
978-1-108-98689-2 (ISBN)
Cambridge University Press (Verlag)
978-1-108-98689-2 (ISBN)
Dimension reduction offers researchers and scholars the ability to make complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace.
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.
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.
| Erscheinungsdatum | 28.07.2021 |
|---|---|
| Reihe/Serie | Elements in Quantitative and Computational Methods for the Social Sciences |
| Zusatzinfo | Worked examples or Exercises |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Gewicht | 160 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Sozialwissenschaften | |
| ISBN-10 | 1-108-98689-7 / 1108986897 |
| ISBN-13 | 978-1-108-98689-2 / 9781108986892 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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