Multivariate Exploratory Data Analysis (MEDA) Toolbox for Matlab

Abstract

The Multivariate Exploratory Data Analysis (MEDA) Toolbox in Matlab is a set of multivariate analysis tools for the exploration of data sets. In the MEDA Toolbox, traditional exploratory plots based on Principal Component Analysis (PCA) or Partial Least Squares (PLS), such as score, loading and residual plots, are combined with new methods like MEDA, oMEDA and SVI plots. The latter are aimed at solving some of the limitations found in the former to adequately extract conclusions from a data set. Also, other useful tools such as cross-validation algorithms, Multivariate Statistical Process Control (MSPC) charts and data simulation/approximation algorithms (ADICOV) are included in the toolbox. Finally, most of the exploratory tools are extended for their use with very large data sets (Big Data), with unlimited number of observations.

Publication
Chemometrics and Intelligent Laboratory Systems
Rafael A. Rodríguez-Gómez
Rafael A. Rodríguez-Gómez
Assistant Professor

My research interests include network security, the early detection of new threats and adversarial machine learning attacksdefense methods in the cybersecurity field.