Modern dimension reduction:
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...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2021
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| Schlagwörter: | |
| 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 |
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| isbn | 9781108981767 |
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| spelling | Waggoner, Philip D. ca. 20./21. Jh. (DE-588)1228365040 aut Modern dimension reduction Philip D. Waggoner Cambridge Cambridge University Press 2021 1 Online-Ressource (86 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 26 Jul 2021) 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 Dimension reduction (Statistics) Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Computational social science (DE-588)1249405939 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Datenverarbeitung (DE-588)4011152-0 s R Programm (DE-588)4705956-4 s Computational social science (DE-588)1249405939 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-108-98689-2 https://doi.org/10.1017/9781108981767 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Waggoner, Philip D. ca. 20./21. Jh Modern dimension reduction Dimension reduction (Statistics) Datenverarbeitung (DE-588)4011152-0 gnd R Programm (DE-588)4705956-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Computational social science (DE-588)1249405939 gnd |
| subject_GND | (DE-588)4011152-0 (DE-588)4705956-4 (DE-588)4193754-5 (DE-588)1249405939 |
| title | Modern dimension reduction |
| title_auth | Modern dimension reduction |
| title_exact_search | Modern dimension reduction |
| title_full | Modern dimension reduction Philip D. Waggoner |
| title_fullStr | Modern dimension reduction Philip D. Waggoner |
| title_full_unstemmed | Modern dimension reduction Philip D. Waggoner |
| title_short | Modern dimension reduction |
| title_sort | modern dimension reduction |
| topic | Dimension reduction (Statistics) Datenverarbeitung (DE-588)4011152-0 gnd R Programm (DE-588)4705956-4 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Computational social science (DE-588)1249405939 gnd |
| topic_facet | Dimension reduction (Statistics) Datenverarbeitung R Programm Maschinelles Lernen Computational social science |
| url | https://doi.org/10.1017/9781108981767 |
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