Table 1.

Principles of the dimensionality reduction techniques used in the classification and prediction algorithms.

Method	Method Abbrev.	Components derivation
Principal Component Analysis	PCA	Maximizes overall dataset variance without considering between-class variance
Partial Least Squares	PLS	Maximizes between-class variance without considering within-class variance
Maximum Margin Criterion	MMC	Maximizes between-class variance, while minimizing within-class variance
Linear Discriminant Analysis	LDA	Maximizes ratio of between- and within-class variation while the number of samples is greater than the number of variables
Support Vector Machines	SVM	Maximizes the margin of separation between the classes

Methods, their respective abbreviation and a descriptive derivation of their components to obtain a reduced dimensionality space. PCA and LDA were used in combination with each other or with other methods to achieve the combinatory methods: PCA-LDA and MMC-LDA.