Descriptive linear discriminant analysis

So fiir, we have described only situations with two classes. The method can also be applied to K classes. It is then sometimes called descriptive linear discriminant analysis. In this case the weight vectors can be shown to be the eigenvectors of the matrix ... 

We haveemployed a variety of unsupervised and supervised pattern recognition methods such as principal component analysis, cluster analysis, k-nearest neighbour method, linear discriminant analysis, and logistic regression analysis, to study such reactivity spaces. We have published a more detailed description of these investigations. As a result of this, functions could be developed that use the values of the chemical effects calculated by the methods mentioned in this paper. These functions allow the calculation of the reactivity of each individual bond of a molecule. 

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