Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
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CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
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The complete theory for Fisher and dual discriminant analysis is presented as the background of the novel algorithms. LDA is found as composition of projection onto the singular subspace for within-class normalised data with the projection onto the singular subspace for between-class normalised data. The dual LDA consists of those projections applied in reverse order. The experiments show that using suitable composition of dual LDA transformations gives as least as good results as recent state-of-the-art solutions.