Weighted Fisher Discriminant Analysis in the Input and Feature Spaces [article]

Benyamin Ghojogh, Milad Sikaroudi, H.R. Tizhoosh, Fakhri Karray, Mark Crowley
2020 arXiv   pre-print
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
more » ... 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.
arXiv:2004.01857v1 fatcat:uqsaqubyxbbyxloc3mepwyuteq