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 unsuper...

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Beteilige Person:	Waggoner, Philip D. ca. 20./21. Jh (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	Englisch
Veröffentlicht:	Cambridge Cambridge University Press 2021
Schlagwörter:	Dimension reduction (Statistics) Datenverarbeitung R > Programm Maschinelles Lernen Computational social science
Links:	https://doi.org/10.1017/9781108981767 https://doi.org/10.1017/9781108981767 https://doi.org/10.1017/9781108981767
Zusammenfassung:	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
Beschreibung:	Title from publisher's bibliographic system (viewed on 26 Jul 2021)
Umfang:	1 Online-Ressource (86 Seiten)
ISBN:	9781108981767
DOI:	10.1017/9781108981767