Generalized LDA using relevance weighting and evolution strategy

In pattern classification area, linear discriminant analysis (LDA) is one of the most traditional methods to find a linear solution to the feature extraction problem, which maximise the ratio between between-class scatter and the within class scatter (Fisher´s criterion). We propose a variant of LDA which incorporates the class conjunctions thereby making LDA more robust for the problems in which the within class scatter is quite different from one class to another, while retaining all the merits of conventional LDA. We also integrate an evolutionary search procedure in our algorithm to make it more unbiased to the training samples and to improve the robustness.