LDA and Dimensionality Reduction [closed]

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I have dataset consisting of about 300 objects with 84 features for each object. The objects are already separated into two classes. With PCA I´m able to reduce the dimensionality to about 24. I´m using 3 principle components covering about 96% of the variance of the original data. The problem I have is that PCA doesnt care about the ability to separate the classes from each other. Is there a way to combine PCA for reducing feature space and LDA for finding a discriminance function for those two classes ? Or is there a way to use LDA for finding the features that separate two classes in threedimensional space in the best manner ?

I´m kind of irritated because I found this paper but I´m not really understanding. http://faculty.ist.psu.edu/jessieli/Publications/ecmlpkdd11_qgu.pdf

Thanks in advance.

Answer 1

You should have a look at this article on principle component regression (PCR, what you want if the variable to be explained is scalar) and partial least squares regression (PLSR) with MATLAB's statistics toolbox. In PCR essentially, you choose the principal components as they most explain the dependent variable. They may not be the ones with the largest variance.