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.

List of contents

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.

Summary

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.

Product details

• Authors: Philip D. Waggoner, Philip D. (University of Chicago) Waggoner
• Publisher: Cambridge University Press ELT
• Languages: English
• Product format: Paperback / Softback
• Released: 31.07.2021
• EAN: 9781108986892
• ISBN: 978-1-108-98689-2
• No. of pages: 75
• Series: Elements in Quantitative and Computational Methods for the Social Sciences
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > General, dictionaries; Social sciences, law, business > Political science > Political science and political education; 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