Modern Dimension Reduction

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.

Sommario

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.

Riassunto

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.

Dettagli sul prodotto

• Autori: Philip D. Waggoner, Philip D. (University of Chicago) Waggoner
• Editore: Cambridge University Press ELT
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 31.07.2021
• EAN: 9781108986892
• ISBN: 978-1-108-98689-2
• Pagine: 75
• Serie: Elements in Quantitative and Computational Methods for the Social Sciences
• Categorie: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Tematiche generali, enciclopedie; Scienze sociali, diritto, economia > Scienze politiche > Scienze politiche e cittadinanza attiva; Data Mining, POLITICAL SCIENCE / General, Coding theory & cryptology, Database design and theory, Research methods: general, Coding theory and cryptology, Data capture and analysis, Data Capture & Analysis, Database design & theory