Partial Least Squares Discriminant Analysis (PLS-DA) applied to smectites classification

J. M. Sánchez Santos, M. J. Rivas López, A. Lorenzo Hernández, M. Suárez Barrios

PLS-DA is a machine learning tool that combines PLS regression and PCA. Through PLS regression a model describing the relationship between a categorical variable and several numerical variables is provided and this model is used for the dimensionality reduction with a PCA, in such a way that the underlying structure of the data is described and the discrimination between the categories is maximized. PLS-DA involves several steps, cross-validation is an important step in using PLS-DA as a feature selector, classifier or even just for visualization.
Smectites are important industrial minerals with great variability in their chemical composition. Their crystallo-chemistry of major elements is well known, but it is not known if trace elements are related to the structure. PLS-DA allows us to find the set of elements that best discriminate between two crystallo-chemical categories of smectites (dioctahedral or trioctahedral) to conclude certain trace elements are related to their structure.

Palabras clave: pls regression, discriminant analysis, dimensionality reduction, feature selection, smectites

Programado

Pósteres II
9 de noviembre de 2023 11:40
CC: Sala Pausa Café