etienne.demontalivet's profile - activity

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...

If anyone can approve :) ?

Etienne

Thank you berak, that's true, sorry I missed that...

Hello,

I'm about to start a project using LDA classification. I have started reading about opencv and the LDA class, but they are still some grey areas for me compare to the theory I have read here and the associated example (1) :

• I was expected that the LDA algorithm would give me discriminant functions for each classes I have trained. That way, I could have used them to predict the class of my testing data, but it seems that the outputs are eigenVectors / Values. How can I use it ?

I have seen on this thread (2) that they perform a classic L2 norm to find the closest neighbour in the lda-subspace to predict, but I can't find any theory explanation about LDA talking about this part.

• My other point is about the processing of the LDA class. The main processing start line 986, here (3), and I can't see any covariance matrix, which seems to be a main operation in LDA processing (sorry if I missed it, opencv annotation is totally new for me).

If anyone could enlight me about how to use this LDA :) Thank you !

Etienne

LINKS : (sorry for removing the 'http' part, I've no right for direct links...)

(1) : people.revoledu.com/kardi/tutorial/LDA/LDA.html

(1) : people.revoledu.com/kardi/tutorial/LDA/Numerical%20Example.html

(2) : answers.opencv.org/question/64165/how-to-perform-linear-discriminant-analysis-with-opencv/

(3) : github.com/opencv/opencv/blob/master/modules/core/src/lda.cpp