Protocol for Correspondence Analysis (CA) or Principal Component Analysis (PCA).
CA is the preferred method of finding inter-image variations. PCA computes the distance between data vectors with Euclidean distances, while CA uses Chi-squared distance. CA is superior because it ignores differences in exposure between images, eliminating the need to rescale between images. In contrast, PCA seems to be more robust: less likely to be trapped in an infinite loop of numerical inaccuracy.
For more info see: [[http://spider.wadsworth.org/spider_doc/spider/docs/techs/classification/tutorial.html#CAPCA][SPIDER MDA documentation]]
capcaStep(analysisType, numberOfFactors, maskType)¶
Apply the selected filter to particles. Create the set of particles.
Convert the input mask if needed.