Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data
Applications
2023-11-14 v1
Abstract
The recent development of more sophisticated spectroscopic methods allows acqui- sition of high dimensional datasets from which valuable information may be extracted using multivariate statistical analyses, such as dimensionality reduction and automatic classification (supervised and unsupervised). In this work, a supervised classification through a partial least squares discriminant analysis (PLS-DA) is performed on the hy- perspectral data. The obtained results are compared with those obtained by the most commonly used classification approaches.
Keywords
Cite
@article{arxiv.1806.09347,
title = {Partial least squares discriminant analysis: A dimensionality reduction method to classify hyperspectral data},
author = {Mario Fordellone and Andrea Bellincontro and Fabio Mencarelli},
journal= {arXiv preprint arXiv:1806.09347},
year = {2023}
}