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PLS-DA, on the contrary, deals with an implicitly closed universe (since the Y variables have a constant sum) so that it ignores the possibility of strangers. However, this has the advantage to make the method more robust to class inhomogeneities, since what matters most in class differences. Critical Aspects of Supervised Pattern Recognition Methods for Interpreting Compositional Data 35 In compositional data, as pointed out Berrueta et al. (2007), the main problem is class overlap, but with a suitable feature selection and adequate sample size, good classification performances can be achieved.
37, pp. 36-48. J. L. (1998). Outlier detection in multivariate analytical chemical data. Analytical Chemistry. Vol. 70, pp. 2372-2379. ; Barcelo-Vidal, C. (2003). Isometric logratio transformation for compositional data analysis. Mathematical Geology. Vol. 35, pp. 279-300. J. R. (1995). Classification of near-infrared spectra using wavelength distances: Comparisons to the Mahalanobis distance and Residual Variance methods. Analytical Chemistry. 67, pp. 160-166. G. (2007). Use and misuse of supervised pattern recognition methods for interpreting compositional data.
Deterministic/Probabilistic techniques: A deterministic method classifies an object in one and only one of the training classes and the degree of reliability of this decision is not measured. Probabilistic methods provide an estimate of the reliability of the classification decision. KNN, MLP, SVM and CAIMAN are deterministic. , PNN where a Bayesian decision is implemented). Linear/Non-Linear separation boundaries: Here our attention is focused on the mathematical form of the decision boundary.