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In a special case, where we used plain LDA, we found that the eigenvectors corresponding to zero eigenvalues were still useful. Hence, if our LDA would return 35 non-zero eigenvalues, it was still better to work with 50 eigenvectors
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I totally agree. It is also conceptually wrong to use LBP as image preprocessing. Nevertheless, we have it in our list of image preprocessors, and it is the best preprocessing for some algorithms such as PCA or LDA.
Printing a warning seems to be OK, but forcing the lda_dim to the rank is wrong in my eyes.Maybe it would be better to make the strip_to_rank https://gitlab.idiap.ch/bob/bob.bio.gmm/blob/0ef9210cd84c1e92b15347a953fc7d29b595a0c1/bob/bio/gmm/algorithm/IVector.py#L79 a parameter to the algorithm. I am OK with changing the default to True. Add some documentation that setting it to False might result in unreliable eigenvectors.I think, we also should change the default value of lda_dim to lda_dim=None. In combination with strip_to_rank=True we should get the optimal default behavior.
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