Jump to ContentJump to Main Navigation
Three-Dimensional Electron Microscopy of Macromolecular AssembliesVisualization of Biological Molecules in Their Native State$
Users without a subscription are not able to see the full content.

Joachim Frank

Print publication date: 2006

Print ISBN-13: 9780195182187

Published to Oxford Scholarship Online: April 2010

DOI: 10.1093/acprof:oso/9780195182187.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2020. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 03 August 2020

Multivariate Data Analysis and Classification of Images

Multivariate Data Analysis and Classification of Images

Chapter:
(p.145) 4 Multivariate Data Analysis and Classification of Images
Source:
Three-Dimensional Electron Microscopy of Macromolecular Assemblies
Author(s):

Joachim Frank

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195182187.003.0004

This chapter is concerned with the need for classification of images to deal with either the co-occurrence of difference view angles, conformational/compositional heterogeneity of the sample, or both. As a start, techniques of multivariate data analysis such as principal component analysis and correspondence analysis are introduced, which sharply reduce the dimensionality of the problem. In the resulting factorial representations of images, variational trends and the presence of clustering can be readily recognized. Application of automated classification is possible in this new space; three techniques of unsupervised classification are described: K-means, hierarchical ascendant classification, and self-organized maps. The chapter closes by introducing supervised classification, which utilizes similarity to two or more template or reference images in order to determine class membership.

Keywords:   correspondence analysis, heterogeneity, hierarchical ascendant classification, K-means, principal component analysis, self-organized maps, supervised classification

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .