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I've looked into Fisher lda today, and understood what I missed :

• There is no discriminant function as output of fisher LDA, discriminant functions are another area of research, and there are different approaches to predict the incoming datas after training the classifier.
• eigenvectors / values maximize the criterion function :

J = ("space"/"distance" between projected averages)² / (sum of scatters²)

which tends to represent the distance between the different classified groups. And so the eigenvectors give the directions which best separate those groups.

• The comparison I've made between openCV and the two first links is irrelevant, because one is LDA and the other Fisher LDA....

• Finally, as berak pointed, there is indeed a covariance matrix I did'nt see...

Thank you !

Etienne

I've looked into Fisher lda today, and understood what I missed :

• There is no discriminant function as output of fisher LDA, LDA (only a "good choice" which would be the hyperplane between projections of the two means), discriminant functions are another area of research, and there are different approaches to predict the incoming datas after training the classifier.
• eigenvectors / values maximize the criterion function :

J = ("space"/"distance" between projected averages)² / (sum of scatters²)

which tends to represent the distance between the different classified groups. And so the eigenvectors give the directions which best separate those groups.

• The comparison I've made between openCV and the two first links is irrelevant, because one is LDA and the other Fisher LDA....

• Finally, as berak pointed, there is indeed a covariance matrix I did'nt see...

Thank you !

Etienne

I've looked into Fisher lda today, and understood what I missed :

• There is no discriminant function as output of fisher LDA (only a "good choice" which would be the hyperplane between projections of the two means), discriminant functions are another area of research, and there are different approaches to predict the incoming datas after training the classifier.
• eigenvectors / values maximize the criterion function :

J = ("space"/"distance" between projected averages)² / (sum of scatters²)

which tends to represent the distance between the different classified groups. And so the eigenvectors give the directions which best separate those groups.

• The comparison I've made between openCV and the two first links is irrelevant, because one is LDA and the other Fisher LDA....

• Finally, as berak pointed, there is indeed a covariance matrix I did'nt see...

Thank you !If anyone can approve :) ?

Etienne