Modern Dimension Reduction - Couverture souple

Waggoner, Philip D.

Couverture souple

ISBN 10 : 1108986897 ISBN 13 : 9781108986892

Editeur : Cambridge University Press, 2021

Synopsis

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.

Détails de l'édition

• Éditeur: Cambridge University Press
• Date d'édition: 2021
• Langue: anglais
• ISBN 10: 1108986897
• ISBN 13: 9781108986892
• Reliure: Broché
• Nombre de pages: 98
• Coordonnées du fabricant: non disponible
• Personne responsable: Avery; https://www.avery.fr/; ; chemin des peupliers 27; Dardilly; France