Linear Discriminant Analysis and Fisher Faces [closed]

asked 2013-10-25 13:50:52 -0500

Andrea00001 gravatar image

updated 2013-10-25 14:15:40 -0500

berak gravatar image

"The Principal Component Analysis (PCA), which is the core of the Eigenfaces method, finds a linear combination of features that maximizes the total variance in data. While this is clearly a powerful way to represent data, it doesn’t consider any classes and so a lot of discriminative information may be lost when throwing components away." (Open CV)

What is mean by "CLASSES" here????

" Linear Discriminant Analysis maximizes the ratio of between-classes to within-classes scatter, instead of maximizing the overall scatter. The idea is simple: same classes should cluster tightly together, while different classes are as far away as possible from each other in the lower-dimensional representation.

in here also what is mean by CLASSES????

Can some one please explain this in image processing view thanx

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Closed for the following reason question is not relevant or outdated by sturkmen
close date 2020-10-20 09:47:58.158705