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Three-Dimensional Electron Microscopy of Macromolecular AssembliesVisualization of Biological Molecules in Their Native State$
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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

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 27 September 2021

Multivariate Data Analysis and Classification of Images

Multivariate Data Analysis and Classification of Images

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

Joachim Frank

Oxford University Press

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

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