  Fisher Discriminant Analysis (FDA) is a subspace learning method which
minimizes and maximizes the intra- and inter-class scatters of data,
respectively. Although, in FDA, all the pairs of classes are treated the same
way, some classes are closer than the others. Weighted FDA assigns weights to
the pairs of classes to address this shortcoming of FDA. In this paper, we
propose a cosine-weighted FDA as well as an automatically weighted FDA in which
weights are found automatically. We also propose a weighted FDA in the feature
space to establish a weighted kernel FDA for both existing and newly proposed
weights. Our experiments on the ORL face recognition dataset show the
effectiveness of the proposed weighting schemes.
