

Linear Discriminant Analysis bit by bit - rasbt
http://sebastianraschka.com/Articles/2014_python_lda.html

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jvm
Note that LDA likely does not give the optimal separating hyperplane when
minimizing out-of-sample error. That honor likely belongs to SVMs, and in
practice tweaking the kernel and identifying relevant non-linear relationships
generally become more important to finding a reasonable classification
boundary.

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rasbt
Yes, I see Linear Discriminant Analysis more as useful tool for pre-processing
and dimensionality reduction rather than using it as classifier

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anateus
Just a note that LDA is also used as a common acronym for Latent Dirichlet
Allocation:
[http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation](http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation)

