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Principal Components Analysis of Multispectral Image Data

Published online by Cambridge University Press:  14 March 2018

Brent Neal  and
John C. Russ
Brent Neal*
Affiliation:
Reindeer Graphics, Inc.
John C. Russ
Affiliation:
Reindeer Graphics, Inc.
*
brent@reindeergraphics.com

Extract

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Principal components analysis of multivariate data sets is a standard statistical method that was developed in the early halt or the 20th century. It provides researchers with a method for transforming their source data axes into a set of orthogonal principal axes and ranks. The rank for each axis in the principal set represents the significance of that axis as defined by the variance in the data along that axis. Thus, the first principal axis is the one with the greatest amount of scatter in the data and consequently the greatest amount of contrast and information, while the last principal axis represents the least amount of information.

Type
Research Article
Information
Microscopy Today , Volume 12 , Issue 5 , September 2004 , pp. 36 - 39
Copyright
Copyright © Microscopy Society of America 2004