Classification normalized vector difference

In the example given here, the sample spectra were sufficiently different to allow classification by a relatively simple linear model (LDA). However, this type of modelling may not be successful if the sample spectra were more similar to each other. In addition, LDA does not perform well if the distribution of the data is non-normal. PLS-DA works slightly better in this situation, but generally kernel methods (e.g. Support Vector Machines) are necessary if the dataset is substantially nonlinear. 

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