### Linear Discriminant Analysis and Fisher Faces

"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
opencv image-processing matching emgucv feature-extraction
share|edit|delete|flag~~

asked just now
user2921008
1thanx